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Updated: Jul 10, 2025

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Published on: March 25, 2020

Modeling the effect of spatial structure on solid tumor evolution and ctDNA composition.

Thomas Rachman^1,2, David Bartlett³, William Laframboise³

¹Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.

Biorxiv : the Preprint Server for Biology

|November 21, 2023

Summary

This summary is machine-generated.

Spatially variable cell death in tumors can distort circulating tumor DNA (ctDNA) profiles, potentially misrepresenting cancer evolution and treatment response. This bias in liquid biopsies may lead to inaccurate clinical decisions.

Keywords:

ctDNA spatial evolution tumor DNA shedding tumor evolution tumor growth model

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Published on: September 13, 2022

Area of Science:

Oncology
Genomics
Computational Biology

Background:

Circulating tumor DNA (ctDNA) is crucial for real-time cancer monitoring, diagnosis, and prognosis.
Current ctDNA analysis primarily relies on DNA from apoptotic or necrotic cells.
Solid tumors exhibit spatially heterogeneous cell death rates due to chemotherapy and immune infiltration.

Approach:

Utilized a stochastic evolutionary model of boundary-driven tumor growth.
Investigated the impact of edge-localized cell death on tumor evolution and ctDNA composition.
Analyzed how spatial shedding biases affect clone representation, mutation accumulation, and variant allele frequencies (VAFs).

Key Points:

Elevated cell death at tumor edges can over-represent invasive clones in ctDNA.
Spatial shedding biases can inflate subclonal VAFs and mimic increased clonal diversity.
Quiescent tumors can exhibit similar biases but with reduced ctDNA detectability.

Conclusions:

Spatially structured cell death can lead to highly biased ctDNA profiles in liquid biopsies.
Biased ctDNA profiles may enhance detection of expanding clones but risk targeting subclonal variants.
Understanding spatially variable cell death is critical for accurate interpretation of liquid biopsy data and clinical decision-making.