Predictive nonlinear modeling of malignant myelopoiesis and tyrosine kinase inhibitor therapy

Chronic myeloid leukemia (CML) is a blood cancer characterized by dysregulated production of maturing myeloid cells driven by the product of the Philadelphia chromosome, the BCR-ABL1 tyrosine kinase. Tyrosine kinase inhibitors (TKIs) have proved effective in treating CML, but there is still a cohort of patients who do not respond to TKI therapy even in the absence of mutations in the BCR-ABL1 kinase domain that mediate drug resistance. To discover novel strategies to improve TKI therapy in CML, we developed a nonlinear mathematical model of CML hematopoiesis that incorporates feedback control and lineage branching. Cell-cell interactions were constrained using an automated model selection method together with previous observations and new in vivo data from a chimeric BCR-ABL1 transgenic mouse model of CML. The resulting quantitative model captures the dynamics of normal and CML cells at various stages of the disease and exhibits variable responses to TKI treatment, consistent with those of CML patients. The model predicts that an increase in the proportion of CML stem cells in the bone marrow would decrease the tendency of the disease to respond to TKI therapy, in concordance with clinical data and confirmed experimentally in mice. The model further suggests that, under our assumed similarities between normal and leukemic cells, a key predictor of refractory response to TKI treatment is an increased maximum probability of self-renewal of normal hematopoietic stem cells. We use these insights to develop a clinical prognostic criterion to predict the efficacy of TKI treatment and design strategies to improve treatment response. The model predicts that stimulating the differentiation of leukemic stem cells while applying TKI therapy can significantly improve treatment outcomes.

ApoJ-Glyc, an early marker of myocardial ischaemia, rapidly maps improved myocardial perfusion in STEMI patients undergoing successful primary PCI

Background

Previous studies using experimental models and clinical retrospective samples have pointed to a potential role of glycosylated apolipoprotein J (ApoJ-Glyc) as a marker for the early detection of myocardial ischaemia. Ischaemia induces intracellular accumulation of non-glycosylated ApoJ that mirrors the reduction in ApoJ-Glyc serum levels in patients presenting with ST-segment elevation myocardial infarction (STEMI).

Purpose

The EDICA (Early Detection of Myocardial Ischaemia in Suspected Acute Coronary Syndromes by Apo J-Glyc as a Novel Pathologically based Ischaemia Biomarker) clinical trial assessed the performance of ApoJ-Glyc as a biomarker for the early detection of myocardial ischaemia in patients attending the A&E department with chest pain suggestive of acute coronary syndrome. Here, we report the observed changes in ApoJ-Glyc concentration in STEMI patients at baseline and after primary percutaneous coronary intervention (PPCI).

Methods

The EDICA trial, a multi-centre (10 sites), international, in vitro diagnostic study, assessed 404 patients attending the A&E department with suspected ACS. Of these, 291 patients had a final diagnosis of “non-ischaemic” event and 113 of “ischaemic” event. The main inclusion criterion was chest pain of suspected cardiac origin. Blood samples were obtained for the simultaneous assessment of high sensitivity-troponin and ApoJ-Glyc on admission (time 0) and at 1h and 3h thereafter. Two different glycosylated variants of ApoJ (ApoJ-GlycA2 and ApoJ-GlycA6) were analyzed with a novel ELISA in serum samples. Of the “ischaemic” patients, 33 had STEMI, of whom 85% underwent PPCI.

Results

As expected, in the presence of myocardial ischaemia, time 0 ApoJ-GlycA2 and ApoJ-GlycA6 serum levels decreased by 34% and 48%, respectively in STEMI patients, compared with non-ischaemic patients, i.e. ApoJ-GlycA2 in STEMI: 66 [52–95] μg/ml vs. non-ischaemic: 100 [72–131] μg/ml; P=0.0002; ApoJ-GlycA6 STEMI: 38 [34–67] vs. non-ischaemic: 73 [56–95] μg/ml; P<0.0001. ApoJ-GlycA6 showed a discriminating ability for the presence of STEMI with a 67% sensitivity and a 83% specificity (AUC=0.747, cut-off of 50μg/ml). In STEMI patients in whom PPCI successfully restored TIMI 3 flow, ApoJ-Glyc levels increased rapidly and significantly compared with time 0 levels (ApoJ-GlycA2: P=0.02 and P=0.003 for 1h and 3h; ApoJ-GlycA6: P=0.02 and P=0.002 for 1h and 3h) and compared to patients in whom PPCI was not performed (Table 1).

Conclusions

ApoJ-Glyc concentrations are reduced in STEMI patients on admission and increase rapidly after improved perfusion with PPCI, pointing to a potential role of this biomarker in the early detection of reversible ischaemia and the mapping of reversible changes. The mechanisms whereby ApoJ-Glyc levels rapidly and markedly increase after PPCI are speculative at present and deserve further investigation, together with the potential prognostic value of ApoJ-Glyc in this setting.

Funding Acknowledgement

Type of funding sources: Public grant(s) – EU funding. Main funding source(s): European Commission 2020-EIC-SMEInstrument Phase 2; Spanish Ministry of Science, Innovation and Universities

Using artificial intelligence for device identification and characterization in angiographic sequences of TAVI procedures as radiomic biomarkers for prosthesis deterioration

Introduction

Transcatheter Aortic Valve Implantation (TAVI) has been established as the preferential option for patients suffering from symptomatic severe aortic stenosis with high surgical risk. Due to the implantation of prosthesis on younger patients, the durability of TAVI devices becomes a crucial issue, especially beyond 5 years of follow-up [1]. Therefore, an application of growing interest is the assessment of the prosthesis deterioration, which is directly related to the measurement of the stent tip deflection [2]. In order to extract this parameter from angiographic sequences, a prior requirement relies on the automatic detection of the prosthesis in the image, pointing directly to the use of Artificial Intelligence.

Purpose

Automatically segment TAVI prostheses in angiographic sequences using Deep Learning [3]. The aim is allowing the extraction of information from these images, as the measurement of the stent tip deflection for evaluating the prosthesis deterioration, to improve the clinical practice.

Methods

A U-Net, which is a Deep Learning architecture designed for the analysis of biomedical imaging, has been trained for the segmentation of TAVI prostheses in angiographies [3]. The self-built dataset includes 50 sequences captured during TAVI from a population of 15 patients, obtaining 2827 frames (examples provided in Fig. 1). This dataset is randomly split into training and test sets using an 80/20 ratio. The U-Net is trained using the training set and its annotations (manually generated), optimised during 50 epochs using Adam optimiser, and binary cross-entropy as the cost function. Once the model has been trained, it is used for the segmentation of the images in the test set.

Results

The segmentation results have been evaluated in separate subsets of frames extracted from the test set, regarding the stage of the procedure they correspond. Such subsets are aortic root previous to the device delivery (Fig. 1A), prosthesis deployment (Fig. 1B), prosthesis fully-expanded (Fig. 1C), and contrast agent injection (Fig. 1D). Fig. 2 provides examples of the results obtained for the images in Fig. 1, showing the original frames and the segmentation result overlaid. The results have been evaluated in terms of different classification metrics, obtaining the mean values in the following ranges for the four groups: accuracy 0.99–1.00, recall 0.70–0.97, and area under the receiver operating characteristic curve (AUROC) 0.85–0.98.

Conclusion

U-Net has been successfully applied to angiographic imaging captured during TAVI procedure for segmenting the prosthesis, with AUROC values between 0.85 and 0.98. The prosthesis identification and characterization allow the extraction of imaging biomarkers of great interest that can be used for the evaluation of the prosthesis deterioration.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Axencia Galega de Innovaciόn (GAIN) (Code Numbers IN845D-2020/29 and IN607B-2021/18)

Optimal experimental design for mathematical models of haematopoiesis

The haematopoietic system has a highly regulated and complex structure in which cells are organized to successfully create and maintain new blood cells. It is known that feedback regulation is crucial to tightly control this system, but the specific mechanisms by which control is exerted are not completely understood. In this work, we aim to uncover the underlying mechanisms in haematopoiesis by conducting perturbation experiments, where animal subjects are exposed to an external agent in order to observe the system response and evolution. We have developed a novel Bayesian hierarchical framework for optimal design of perturbation experiments and proper analysis of the data collected. We use a deterministic model that accounts for feedback and feedforward regulation on cell division rates and self-renewal probabilities. A significant obstacle is that the experimental data are not longitudinal, rather each data point corresponds to a different animal. We overcome this difficulty by modelling the unobserved cellular levels as latent variables. We then use principles of Bayesian experimental design to optimally distribute time points at which the haematopoietic cells are quantified. We evaluate our approach using synthetic and real experimental data and show that an optimal design can lead to better estimates of model parameters.

Spatial pattern formation in microtubule post-translational modifications and the tight localization of motor-driven cargo

Microtubule (MT) "age" can be interpreted as nucleotide state, lattice defects, or post-translational modification (PTM) such as acetylation and detyrosination. In all three cases, these have been recently shown to have functionally-important effects on the dynamics of MT arrays, and can present spatial and temporal heterogeneity. While mathematical models for MT array densities are well-established, here we present equations describing MT age, defined as the mean time since the MT's building blocks (tubulin) were polymerized from their soluble dimer state. We derive the age equations using a mean first-passage time calculation and two complementary approaches: The continuum limit of spatial discretization model, and an adjoint operator approach. These equations can recapitulate the observation that the oldest (most de-tyrosinated) tubulin in axons is near the middle of axons during neuronal development in chick embryos. Furthermore, PTMs influence motor kinetics up to approximately twofold for off-rates and velocities. Our simulations demonstrate that this relatively weak dependence of motor kinetics is sufficient to target motor cargo to a specific location along the array. This localization is tightly peaked in a way that magnifies the relatively small signal of PTM spatial heterogeneity. Thus, MT age can produce long-range spatial patterning without feedbacks or diffusing signals.

Timing of CD8 T cell effector responses in viral infections

CD8 T cell or cytotoxic T lymphocyte (CTL) responses are an important branch of the immune system in the fight against viral infections. The dynamics of anti-viral CTL responses have been characterized in some detail, both experimentally and with mathematical models. An interesting experimental observation concerns the timing of CTL responses. A recent study reported that in pneumonia virus of mice the effector CTL tended to arrive in the lung only after maximal virus loads had been achieved, an observation that seems at first counterintuitive because prevention of pathology would require earlier CTL-mediated activity. A delay in CTL-mediated effector activity has also been quoted as a possible explanation for the difficulties associated with CTL-based vaccines. This paper uses mathematical models to show that in specific parameter regimes, delayed CTL effector activity can be advantageous for the host in the sense that it can increase the chances of virus clearance. The increased ability of the CTL to clear the infection, however, is predicted to come at the cost of acute pathology, giving rise to a trade-off, which is discussed in the light of evolutionary processes. This work provides a theoretical basis for understanding the described experimental observations.