High-density sampling reveals volume growth in human tumours

Reviewer #1 (Public Review):

Summary:

Arman Angaji and his team delved into the intricate world of tumor growth and evolution, utilizing a blend of computer simulations and real patient data from liver cancer.

Strengths:

Their analysis of how mutations and clones are distributed within tumors revealed an interesting finding: tumors don't just spread from their edges as previously believed. Instead, they expand both from within and the edges simultaneously, suggesting a unique growth mode. This mode naturally indicates that external forces may play a role in cancer cells dispersion within the tumor. Moreover, their research hints at an intriguing phenomenon - the high death rate of progenitor cells and extremely slow pace in growth in the initial phase of tumor expansion. Understanding this dynamic could significantly impact our comprehension of cancer development.

Weaknesses:

It's important to note, however, that this study relies on specific computer models, metrics derived from inferred clones, and a limited number of patient data. While the insights gained are promising, further investigation is essential to validate these findings. Nonetheless, this work opens up exciting avenues for comprehending the evolution of cancers.

Reviewer #2 (Public Review):

Summary:

The article uses a cell-based model to investigate how mutations and cells spread throughout a tumour. The paper uses published data and the proposed model to understand how growth and death mechanisms lead to the observed data. This work provides an insight into the early stages of tumour development. From the work provided here, the results are solid, showing a thorough analysis. However, the work has not fully specified the model, which can lead to some questions around the model's suitability. The article is well-written and presents a very suitable and rigorous analysis to describe the data. The authors did a particularly nice job of the discussion and decision of their "metrics of interest", though this is not the main aim of this work.

Strengths:

Due to the particularly nice and tractable cell-based model, the authors are able to perform a thorough analysis to compare the published data to that simulated with their model. They then used their computational model to investigate different growth mechanisms of volume growth and surface growth. With this approach, the authors are able to compare the metric of interest (here, the direction angle of a new mutant clone, the dispersion of mutants throughout the tumour) to quantify how the different growth models compare to the observed data. The authors have also used inference methods to identify model parameters based on the data observed. The authors performed a rigorous analysis and have chosen the metrics in an appropriate manner to compare the different growth mechanisms.

Weaknesses:

The work contained within this article considers a single cell-based model. While ideally, this is sufficient, results from simulated multi-cellular systems can often be sensitive to the model choice. Performing this work with various other standard models would strengthen the results significantly. This is, however, not an easy task.

Context:

Improved mechanistic understanding into the early developmental stages of tumours will further assist in disease treatment and quantification. Understanding how readily and quickly a tumour is evolving is key to understanding how it will develop and progress. This work provides a solid example as to how this can be achieved with data alongside simulated models.