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Physical Limits of Proximal Tumor Detection via MAGE-A Extracellular Vesicles

A. Sila Okcu, M. Etem Bas, Ozgur B. Akan

TL;DR

This work evaluates the physical feasibility of detecting MAGE-A–bearing microvesicles with a proximal smart-needle sensor in the peri-tumoral interstitial space. By coupling Brownian-dynamics simulations (Smoldyn) with a continuum reaction–diffusion model, it quantifies how ECM tortuosity, secretion rate, and low-copy-number statistics govern detection performance. A threshold-based detector reveals a practical detection radius of about $275~\mu\mathrm{m}$ from the tumour centre (roughly $225~\mu\mathrm{m}$ from the boundary) within a $6000$ s window, above which stochastic false negatives dominate. These results define the physical limits for near-field EV-based diagnostics and inform design choices for minimally invasive peri-tumoral sensors requiring precise placement relative to the tumour.

Abstract

Early cancer detection relies on invasive tissue biopsies or liquid biopsies limited by biomarker dilution. In contrast, tumour-derived extracellular vesicles (EVs) carrying biomarkers like melanoma-associated antigen-A (MAGE-A) are highly concentrated in the peri-tumoral interstitial space, offering a promising near-field target. However, at micrometre scales, EV transport is governed by stochastic diffusion in a low copy number regime, increasing the risk of false negatives. We theoretically assess the feasibility of a smart-needle sensor detecting MAGE-A-positive microvesicles near a tumour. We use a hybrid framework combining particle-based Brownian dynamics (Smoldyn) to quantify stochastic arrival and false negative probabilities, and a reaction-diffusion PDE for mean concentration profiles. Formulating detection as a threshold-based binary hypothesis test, we find a maximum feasible detection radius of approximately 275 micrometers for a 6000 s sensing window. These results outline the physical limits of proximal EV-based detection and inform the design of minimally invasive peri-tumoral sensors.

Physical Limits of Proximal Tumor Detection via MAGE-A Extracellular Vesicles

TL;DR

This work evaluates the physical feasibility of detecting MAGE-A–bearing microvesicles with a proximal smart-needle sensor in the peri-tumoral interstitial space. By coupling Brownian-dynamics simulations (Smoldyn) with a continuum reaction–diffusion model, it quantifies how ECM tortuosity, secretion rate, and low-copy-number statistics govern detection performance. A threshold-based detector reveals a practical detection radius of about from the tumour centre (roughly from the boundary) within a s window, above which stochastic false negatives dominate. These results define the physical limits for near-field EV-based diagnostics and inform design choices for minimally invasive peri-tumoral sensors requiring precise placement relative to the tumour.

Abstract

Early cancer detection relies on invasive tissue biopsies or liquid biopsies limited by biomarker dilution. In contrast, tumour-derived extracellular vesicles (EVs) carrying biomarkers like melanoma-associated antigen-A (MAGE-A) are highly concentrated in the peri-tumoral interstitial space, offering a promising near-field target. However, at micrometre scales, EV transport is governed by stochastic diffusion in a low copy number regime, increasing the risk of false negatives. We theoretically assess the feasibility of a smart-needle sensor detecting MAGE-A-positive microvesicles near a tumour. We use a hybrid framework combining particle-based Brownian dynamics (Smoldyn) to quantify stochastic arrival and false negative probabilities, and a reaction-diffusion PDE for mean concentration profiles. Formulating detection as a threshold-based binary hypothesis test, we find a maximum feasible detection radius of approximately 275 micrometers for a 6000 s sensing window. These results outline the physical limits of proximal EV-based detection and inform the design of minimally invasive peri-tumoral sensors.
Paper Structure (14 sections, 4 equations, 5 figures, 1 table)

This paper contains 14 sections, 4 equations, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methodology
  3. System Representation
  4. Biophysical Parameterisation
  5. Smoldyn Stochastic Simulation
  6. Continuum Reaction–Diffusion PDE
  7. Feasible Detection Radius
  8. Different Parameters
  9. Detection Logic
  10. Results and Analysis
  11. Stochastic Signal Strength and Variance
  12. Validation against Continuum Theory & Comparison with Sensor Detection Limits
  13. Limitations & Future Work
  14. Conclusion

Figures (5)

  • Figure 1: 3D Smoldyn simulation showing the MAGE-A+ tumour source (turquoise), background noise sources (purple), and sensor (orange). Scattered points indicate diffusing MVs.
  • Figure 2: Sensitivity to EV biophysical properties and microenvironment.
  • Figure 3: Operating regime map showing 95% detection contour.
  • Figure 4: Computational modeling of EV transport and detection. (a) Cumulative arrival kinetics at different sensor distances from tumour center ($N_T$). (b) Trade-off between signal magnitude and false negative probability. (c) Detection probability as a function of distance for multiple thresholds. (d) Validation of mean-field PDE against stochastic Smoldyn simulations.
  • Figure 5: Histograms of arrival count distributions (raw $N_{T}$)