Dear Dr. Foo,

We are pleased to inform you that your manuscript 'Understanding patient-derived tumor organoid growth through an integrated imaging and mathematical modeling framework' has been provisionally accepted for publication in PLOS Computational Biology.

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PLOS Computational Biology

Zhaolei Zhang

Section Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: In this manuscript, Gunnarsson et al provide a comprehensive comparison of multiple existing computational models on patient-derived colorectal tumor organoids in exponential growth.

This paper represents important contribution toward establishing experimental and computational models that can explain organoid growth. As preclinical studies of oncology models move from 2D to 3D cell culture, the work presented herein is likely to be applicable and of interest to a wide audience. Overall, this is a well-written and sound study. I have a few comments and critiques that would strengthen this manuscript.

1. Estimates of mutational burden seem over-interpreted and potentially quite error prone. As the authors indicate, the in vivo/patient setting expects to acquire mutations over years, particularly in the absence of therapeutic stress/selection pressure. The authors should use a more sophisticated model here or else remove these analyses from the paper.

2. The preprocessing of 3D image stacks to support incorporation of quantitative image-based features into a computational model is an important aspect of this study. As such, the authors should provide additional details and code from their image analysis pipelines, i.e. for lines 352-358.

3. Code provided to reproduce findings are included in a publicly accessible GitHub repo; however, also providing a version of the code that a reader could deploy on their own data would further enhance the impact of the paper.

4. The associated raw image data may be of interest to the general community. The authors should comment on how they will make it available.

Reviewer #2: This work evaluates the fitting of data from tumor organoids, obtained by employing a high-throughout image deep learning platform, to various growth models. It is an interesting work, providing with significant insight regarding the tumor growth, while it also reports the inter- and intrapatient heterogeneity in the tumor growth parameters. I suggest the publication of this work after the following minor points are addressed.

1) In the Materials and Methods Section, the three datasets UP, US and UK are introduced. However, these acronyms are not defined, and while the genomic analysis for each sample is reported in Table 1, the main differences (e.g. tumor stage) between the different datasets are not discussed from the beginning. This is important, since some of the results differ between datasets.

2) For the fitting evaluation, the Gompertz model is shown to be the best fit, a fact from which we can infer important conclusions e.g., as the authors mention, "cell division occurring uniformly across the organoid, as opposed to be restricted to the outermost cell layer" (as assumed in the von Bertlanffy model). This is a significant central result of the manuscript. However, in Section 3.3, it is stated that in each dataset, many organoids undergo exponential and not Gomperzian growth. The authors should provide more details on the percentage of organoids undergoing exponential growth, as well as for the carrying capacities of the ones undergoing Gompertzian growth (more detailed than Figure 4b, maybe a distribution of carrying capacities for each dataset?).

3) Building upon the previous point, I am curious to see a discussion on what a carrying capacity in the organoid growth means for the growth of the original tumor itself. Does it mean that the tumor itself will reach a carrying capacity? Isn't it expected for tumors in stage 4 (like the ones in UK and UP datasets) to follow an unbounded growth in volume? Or the carrying capacity for the organoids is translated to a carrying capacity for tumors that is high enough for the tumor growth to be considered exponential? In the final paragraph of Section 3, the authors discuss the difference in timescales between organoid and tumor growth. I think that a similar discussion for the carrying capacities is apposite.

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: No