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View article: An algorithmic perspective on deciphering cell–cell interactions with spatial omics data
An algorithmic perspective on deciphering cell–cell interactions with spatial omics data Open
The advent of technologies to measure molecule information from a tissue that retains spatial information paved the way for the development of cell–cell interaction (CCI) methods. Even though these spatial technologies are still in their r…
View article: Adaptive Prototype Learning for Multimodal Cancer Survival Analysis
Adaptive Prototype Learning for Multimodal Cancer Survival Analysis Open
Leveraging multimodal data, particularly the integration of whole-slide histology images (WSIs) and transcriptomic profiles, holds great promise for improving cancer survival prediction. However, excessive redundancy in multimodal data can…
View article: Mathematically mapping the network of cells in the tumor microenvironment
Mathematically mapping the network of cells in the tumor microenvironment Open
Cell-cell interaction (CCI) networks are key to understanding disease progression and treatment response. However, existing methods for inferring these networks often aggregate data across patients or focus on cell-type level interactions,…
View article: Integrating histopathology and transcriptomics for spatial tumor microenvironment profiling in a melanoma case study
Integrating histopathology and transcriptomics for spatial tumor microenvironment profiling in a melanoma case study Open
Local structures formed by cells in the tumor microenvironment (TME) play an important role in tumor development and treatment response. This study introduces SPoTLIghT, a computational framework providing a quantitative description of the…
View article: Agent-based modeling of the prostate tumor microenvironment uncovers spatial tumor growth constraints and immunomodulatory properties
Agent-based modeling of the prostate tumor microenvironment uncovers spatial tumor growth constraints and immunomodulatory properties Open
Inhibiting androgen receptor (AR) signaling through androgen deprivation therapy (ADT) reduces prostate cancer (PCa) growth in virtually all patients, but response may be temporary, in which case resistance develops, ultimately leading to …
View article: Strengths and challenges in current lung cancer care: Timeliness and diagnostic procedures in six Dutch hospitals
Strengths and challenges in current lung cancer care: Timeliness and diagnostic procedures in six Dutch hospitals Open
These insights could aid in improved LC diagnostics and efficient implementation of new techniques like liquid biopsy and artificial intelligence. This may lead to more timely LC care, a decreased number of invasive procedures, less variab…
View article: Agent-based modeling of the prostate tumor microenvironment uncovers spatial tumor growth constraints and immunomodulatory properties
Agent-based modeling of the prostate tumor microenvironment uncovers spatial tumor growth constraints and immunomodulatory properties Open
The data for manuscript "Agent-based modeling of the prostate tumor microenvironment uncovers spatial tumor growth constraints and immunomodulatory properties" are deposited here. FOLDER GrowthFolder contains all biological replicates for …
View article: Agent-based modeling of the prostate tumor microenvironment uncovers spatial tumor growth constraints and immunomodulatory properties
Agent-based modeling of the prostate tumor microenvironment uncovers spatial tumor growth constraints and immunomodulatory properties Open
The data for manuscript "Agent-based modeling of the prostate tumor microenvironment uncovers spatial tumor growth constraints and immunomodulatory properties" are deposited here. FOLDER GrowthFolder contains all biological replicates for …
View article: Multimodal analysis unveils tumor microenvironment heterogeneity linked to immune activity and evasion
Multimodal analysis unveils tumor microenvironment heterogeneity linked to immune activity and evasion Open
Input data and results for the manuscriptMultimodal analysis unveils tumor microenvironment heterogeneity linked to immune activity and evasionCorresponding source code is available from GitHub: https://github.com/ComputationalBiomedicineG…
View article: Multimodal analysis unveils tumor microenvironment heterogeneity linked to immune activity and evasion
Multimodal analysis unveils tumor microenvironment heterogeneity linked to immune activity and evasion Open
Summary The cellular and molecular heterogeneity of tumors is a major obstacle to cancer immunotherapy. Here, we use a systems biology approach to derive a signature of the main sources of heterogeneity in the tumor microenvironment (TME) …
View article: Bilayer Microfluidic Device for Combinatorial Plug Production
Bilayer Microfluidic Device for Combinatorial Plug Production Open
Droplet microfluidics is a versatile tool that allows the execution of a large number of reactions in chemically distinct nanoliter compartments. Such systems have been used to encapsulate a variety of biochemical reactions - from incubati…
View article: Agent-based modeling of the prostate tumor microenvironment uncovers spatial tumor growth constraints and immunomodulatory properties
Agent-based modeling of the prostate tumor microenvironment uncovers spatial tumor growth constraints and immunomodulatory properties Open
Inhibiting androgen receptor (AR) signaling through androgen deprivation therapy (ADT) reduces prostate cancer (PCa) growth in virtually all patients, but response is temporary, and resistance inevitably develops, ultimately leading to let…
View article: Supplementary Video V1 from Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model
Supplementary Video V1 from Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model Open
Supplementary Video V1
View article: FIGURE 6 from Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model
FIGURE 6 from Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model Open
Effect on tumor growth of varying sensitive model parameters. A, Grouped histogram of the repeated sensitivity analysis (five times for each parameter), overlapped by four (differently colored) histograms of the most sensitive parameters: …
View article: Supplementary Figure 3 from Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model
Supplementary Figure 3 from Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model Open
Sensitivity analysis of the most sensitive model parameters
View article: FIGURE 5 from Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model
FIGURE 5 from Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model Open
Comparison between model simulations and histology images (tufting and bridging). A, Pathology slice of a prostate cancer patient (H&E; staining, 400x magnification) showing a “tufted” pattern of growths on the luminal cell layer. B, Model…
View article: Supplementary Table 1 from Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model
Supplementary Table 1 from Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model Open
List of model assumptions
View article: Supplementary Table 2 from Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model
Supplementary Table 2 from Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model Open
List of model parameters
View article: FIGURE 1 from Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model
FIGURE 1 from Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model Open
Overview of the agents and actions they can perform during each model iteration. The simulation starts with luminal cells (LC) and basal cells (BC) that can proliferate, die, or idle, all within physiologic regions and with fixed probabili…
View article: Supplementary Figure 1 from Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model
Supplementary Figure 1 from Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model Open
Experimental data and model parameter fitting
View article: FIGURE 2 from Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model
FIGURE 2 from Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model Open
In silico testing of requirements for tumor maintenance. A, Amount of tumor cells (blue) and percentage of stem cells (orange, dotted) simulated over time under the condition that included only stem cells to maintain tumors. Simulations fo…
View article: Supplementary Table 3 from Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model
Supplementary Table 3 from Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model Open
Parameter values of eight patient groups
View article: Supplementary Table 5 from Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model
Supplementary Table 5 from Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model Open
Patient markers distinguishing the eight different patient classes
View article: FIGURE 7 from Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model
FIGURE 7 from Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model Open
Clinical validation of model predictions for different patient groups. A, Correlation between the simulated tumor growth (simulation time 400 days, 40 simulations per modeled patient group) and the average PFS time for clinical patients as…
View article: FIGURE 3 from Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model
FIGURE 3 from Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model Open
Overview of the starting geometry in 3-fold; a pathology slice, schematic representation, and model geometry visualization. A, A histology slice of a healthy prostatic acinus (H&E; staining, 400x magnification). B, Schematic representation…
View article: FIGURE 5 from Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model
FIGURE 5 from Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model Open
Comparison between model simulations and histology images (tufting and bridging). A, Pathology slice of a prostate cancer patient (H&E; staining, 400x magnification) showing a “tufted” pattern of growths on the luminal cell layer. B, Model…