PREreview of "A 3D microtumour system that faithfully represents ovarian cancer minimal residual disease"

This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/8239029. This review reflects comments and contributions from Teena Bajaj, Pragadeeshwara Rao R, Marina Schernthanner, Ruchika Bajaj, Shreyas Samir Parkhie, Adriana Mena and Arpita Ghosh. Review synthesized by Arpita Ghosh and Garima Jain. This preprint is about creating a 3D model of ovarian cancer. This would help in creating the microenvironment of cancer, and could be used as a model for drug screening. Major comments Strengths - 3D model for ovarian cancer, could be used for screening the drugs. The question - can it replace the requirement of animal work ? Cell viability studies that are done in a qualitative analysis should be done with an assay to give quantitative estimation (like MTT assay) to consolidate the effects. Quantitative estimation of the imaging performed would add to the effects observed with the stainings. The authors mention the importance of extracellular matrix interactions but in my opinion it should be important to take into account the immunological response as well because it is well known that immune cells are involved in the tumour process. The authors may note that some other reports and commercial protocols advise a pre starving step before introducing chemicals or drugs in the cell culture to measure the effect of those. This is not reflected by the authors in their methods when they explain how they prepare OVCAR5 and OVCAR8 cell lines. The authors have done a very elaborate RNA-seq study along with their analysis that adds on to the significance of this study. Although for each signature studied from the transcriptomics data, enrichment plots could be more informative. Some of the conclusions that are drawn from the RNA-seq data should be validated through functional studies to consolidate the claims like migration of 2D/3D cells, proliferation of the cancer, or high resistance. A critical point to consider while determining the IC50 values in 2D or 3D cells is whether the data is normalised to the number of cells and whether they are same in case of the 2D cells and 3D model. An equal number of cells should be considered to validate the claim by the authors. Minor comments In the introduction, "why MRD ovarian cancer is selected with respect to the tumor microenvironment" can be incorporated to leverage the importance of the study. Introduction has great points, however, it is difficult to connect the paragraphs. Proofreading the document for punctuation marks, grammatical errors, missing words, missing references, proper colour coding and mentioning their corresponding experimental sample, figure citation at all relevant places, clarity on every experiment whether performed on 2D or 3D or both and incomplete sentences. Measurements and analysis mentioned in the results or methods should be elaborated more. For example how was the cell size measured, or what exactly is the flowrate ratio of the device used, how the time for hypoxic characteristics is calculated etc. Some graphs in the figure could be replaced with box plots where distribution of points would add on to the observations, like in the drug dosage experiment. It would be useful for readers if a short description for the effects of drugs carboplatin and paclitaxel are included in the main draft. In references, citing similar effects studied on clinical samples would be impactful (even if checked for other cancer types). Comments on reporting Origin/source of the tumor cells should be stated. Suggestions for future studies Similar experiment validated with patient samples could be a very promising venture based on this study. Competing interests The author declares that they have no competing interests