Mechanistic modeling of anti-microRNA-155 therapy combinations in lung cancer

Cave, Joseph; Shinglot, Vrushaly; Butner, Joseph D.; Cristini, Vittorio; Ozpolat, Bulent; Calin, George A.; Dogra, Prashant; Wang, Zhihui

doi:10.1109/embc40787.2023.10341114

Cited by 7 publications

(3 citation statements)

References 18 publications

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“…We developed a multiscale mechanistic model of tumor response to systemic therapies to investigate the translational potential of nanoparticle (NP)-delivered anti-miR-155 therapy for use in combination with standard-of-care chemotherapy and/or ICI immunotherapies in NSCLC. Extending our previous work (17,19,20), the current model comprises two major compartments, as illustrated in Fig. 1 : the plasma compartment, representing systemic circulation and the site of drug administration, and the tumor compartment, containing cancer cells and infiltrating immune cells, such as tumor-associated macrophages (TAMs) and cytotoxic CD8+ T cells that are recruited from the plasma compartment.…”

Section: Resultsmentioning

confidence: 99%

“…Extending our previous work (17,19,20), the current model comprises two major compartments, as illustrated in Fig. 1: the plasma compartment, representing systemic circulation and the site of drug administration, and the tumor compartment, containing cancer cells and infiltrating immune cells, such as tumor-associated macrophages (TAMs) and cytotoxic CD8+ T cells that are recruited from the plasma compartment.…”

Section: Model Development Parameterization and Calibrationmentioning

confidence: 99%

“…Extending our previous modeling framework (17,19,20), the mechanistic model was formulated as a system of ordinary differential equations (ODEs) that incorporate the relevant biological processes and interactions to describe the temporal evolution of key physiological, pathological, and pharmacological variables. As shown in Fig.…”

Section: Mathematical Model Developmentmentioning

confidence: 99%

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Translational modeling-based evidence for enhanced efficacy of standard-of-care drugs in combination with anti-microRNA-155 in non-small-cell lung cancer

Dogra,

Shinglot,

Ruiz-Ramírez

et al. 2024

Preprint

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Elevated microRNA-155 (miR-155) expression in non-small-cell lung cancer (NSCLC) promotes cisplatin resistance and negatively impacts treatment outcomes. However, miR-155 can also boost anti-tumor immunity by suppressing PD-L1 expression. We developed a multiscale mechanistic model, calibrated with in vivo data and then extrapolated to humans, to investigate the therapeutic effects of nanoparticle-delivered anti-miR-155 in NSCLC, alone or in combination with standard-of-care drugs. Model simulations and analyses of the clinical scenario revealed that monotherapy with anti-miR-155 at a dose of 2.5 mg/kg administered once every three weeks has substantial anti-cancer activity. It led to a median progression-free survival (PFS) of 6.7 months, which compared favorably to cisplatin and immune checkpoint inhibitors. Further, we explored the combinations of anti-miR-155 with standard-of-care drugs, and found strongly synergistic two- and three-drug combinations. A three-drug combination of anti-miR-155, cisplatin, and pembrolizumab resulted in a median PFS of 13.1 months, while a two-drug combination of anti-miR-155 and cisplatin resulted in a median PFS of 11.3 months, which emerged as a more practical option due to its simple design and cost-effectiveness. Our analyses also provided valuable insights into unfavorable dose ratios for drug combinations, highlighting the need for optimizing dose regimen to prevent antagonistic effects. Thus, this work bridges the gap between preclinical development and clinical translation of anti-miR-155 and unravels the potential of anti-miR-155 combination therapies in NSCLC.

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Section: Resultsmentioning

confidence: 99%

Section: Model Development Parameterization and Calibrationmentioning

confidence: 99%

Section: Mathematical Model Developmentmentioning

confidence: 99%

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Translational modeling-based evidence for enhanced efficacy of standard-of-care drugs in combination with anti-microRNA-155 in non-small-cell lung cancer

Dogra,

Shinglot,

Ruiz-Ramírez

et al. 2024

Preprint

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Interrater agreement of annotations of epileptiform discharges and its impact on deep learning – A pilot study

Svantesson,

Eklund,

Thordstein

2024

Preprint

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Background: Expert interrater agreement for epileptiform discharges can be moderate. This reasonably will affect the performance when developing classifiers based on annotations performed by experts. In addition, evaluation of classifier performance will be difficult since the ground truth will have a variability. In this pilot study, these aspects were investigated to evaluate the feasibility of conducting a larger study on the subject. Methods: A multi-channel EEG of 78 minutes duration with abundant periodic discharges was independently annotated for epileptiform discharges by two experts. Based on this, several deep learning classifiers were developed which in turn produced new annotations. The agreements of all annotations were evaluated by pairwise comparisons using Cohens kappa and Gwets AC1. A cluster analysis was performed on all periodic discharges using a newly developed version of parametric t-SNE to assess the similarity between annotations. Results: The Cohens kappa values were 0.53 for the experts, 0.52-0.65 when comparing the experts to the classifiers, and 0.67-0.82 for the classifiers. The Gwets AC1 values were 0.92 for the experts, 0.92-0.94 when comparing the experts to the classifiers, and 0.94-0.96 for the classifiers. Although there were differences between all annotations regarding which discharges that had been selected as epileptiform, the selected discharges were mostly similar according to the cluster analysis. Almost all identified epileptiform discharges by the classifiers were also periodic discharges. Conclusions: There was a discrepancy between agreement scores produced by Cohens kappa and Gwets AC1. This was probably due to the skewed prevalence of epileptiform discharges, which only constitutes a small part of the whole EEG. Gwets AC1 is often considered the better option and the results would then indicate an almost perfect agreement. However, this conclusion is questioned when considering the number of differently classified discharges. The difference in annotation between experts affected the learning of the classifiers, but the cluster analysis indicates that all annotations were relatively similar. The difference between experts and classifiers is speculated to be partly due to intrarater variability of the experts, and partly due to underperformance of the classifiers. For a larger study, in addition to using more experts, intrarater agreement should be assessed, the classifiers can be further optimized, and the cluster method hopefully be further improved.

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Does global signal regression alter fMRI connectivity patterns related to EEG activity? An EEG-fMRI study in humans

Xifra-Porxas,

Kassinopoulos,

Prokopiou

et al. 2024

Preprint

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Functional brain connectivity measures extracted from resting-state functional magnetic resonance imaging (fMRI) scans have generated wide interest as potential noninvasive biomarkers. In this context, performing global signal regression (GSR) as a preprocessing step remains controversial. Specifically, while it has been shown that a considerable fraction of global signal variations is associated with physiological and motion sources, GSR may also result in removing neural activity. Here, we address this question by examining the fundamental sources of resting global signal fluctuations using simultaneous electroencephalography (EEG)-fMRI data combined with cardiac and breathing recordings. Our results suggest that systemic physiological fluctuations account for a significantly larger fraction of global signal variability compared to electrophysiological fluctuations. Furthermore, we show that GSR reduces artifactual connectivity due to heart rate and breathing fluctuations, but preserves connectivity patterns associated with electrophysiological activity within the alpha and beta frequency ranges. Overall, these results provide evidence that the neural component of resting-state fMRI-based connectivity is preserved after the global signal is regressed out.

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Mechanistic modeling of anti-microRNA-155 therapy combinations in lung cancer

Cited by 7 publications

References 18 publications

Translational modeling-based evidence for enhanced efficacy of standard-of-care drugs in combination with anti-microRNA-155 in non-small-cell lung cancer

Translational modeling-based evidence for enhanced efficacy of standard-of-care drugs in combination with anti-microRNA-155 in non-small-cell lung cancer

Interrater agreement of annotations of epileptiform discharges and its impact on deep learning – A pilot study

Does global signal regression alter fMRI connectivity patterns related to EEG activity? An EEG-fMRI study in humans

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