Registered report: Discovery and preclinical validation of drug indications using compendia of public gene expression data

1) At the beginning of the Materials and methods section: The authors plan to follow the mice for 11 days instead of 12 days. Is there a good reason to follow the mice one day short? In addition, the experiment contains five cohorts. Among the five cohorts, cohort 2 only has 5 mice while the other 4 cohorts have 14 mice. Please justify.

Figure 4C and Supplemental Figure 1 show data from Days 1 through 11. Although Day 12 is displayed on the graphs, no data is present on that timepoint. We interpreted this to mean that the original authors counted Day 0 as one of the days of monitoring; including Day 0 accounts for the 12 days of monitoring mentioned by the authors and reconciles that statement with the 11 days of data displayed in Figure 4D and Supplemental Figure 1.

For budgetary and ethical reasons, we wished to minimize the number of animals required for these experiments. Thus, we did not use equal sample sizes for the additional positive control (doxorubicin) treatment group when performing our power calculations. These calculations demonstrated that we could use 3 mice in the control group and still achieve 80% power to detect the original data’s effect size. The main aim of the experiment is to test the effect of vehicle (PBS) treatment compared to experimental (cimetidine) treatment, which includes an equal number of mice in each cohort. Considering the unbalanced nature of this design we will be performing a planned comparison between the PBS and doxorubicin outside the framework of the ANOVA, however, the four cohorts that are the main aim of the experiment are analyzed within the balanced ANOVA framework.

2) Power calculation was based on t-test. It is suggested that the authors use two-tailed unequal variance t-test if normality is not violated or the use of Wilcoxon rank-sum test if normality is violated. The authors propose the use of two-way ANOVA followed by t-test for analyzing tumor weight data (in the subsection headed “Confirmatory analysis plan”). Please make sure that the data do not violate the assumptions of ANOVA: normality and homoscedasiticity. If the data do not fit the assumptions well enough, please try to find a data transformation that makes them fit. If this doesn't work, please apply a nonparametric counterpart of ANOVA such as Kruskal–Wallis test. In addition, I suggest the use of contrast within the ANOVA framework instead of t-test if the assumptions of ANOVA are met.

We have added language to the manuscript to clarify that we will perform the normality and homoscedasticity tests. The original data was not shared, so instead summary statistics estimated from the graph presented in Figure 4C and Supplemental Figure 1 were used. This limits what we can ascertain from the original data. We recalculated the samples sizes using a Welch’s t-test instead of a Student’s. The sample size we have planned is still sufficient. This is also true for the non-parametric Wilcoxon rank-sum test. We plan to use the contrast within the ANOVA framework if the assumptions are met, with the exception of the doxorubicin cohort, which will be performed outside the framework because of the unbalanced design.

3) To compare growth curves of tumors, the authors propose ANCOVA followed by Bonferroni corrected t-tests. Please make sure that the data do not violate the assumptions of ANCOVA and perform transformation or use non-parametric ANCOVA if needed.

Pursuant to one of the later comments, we have removed the exploratory analysis by area under the curve from the manuscript.

4) For the additional comparison of PBS-treated A459 tumors to Doxorubicin treated tumors (in the subsection headed “Confirmatory analysis plan” and in the subsection headed “Test family”), I suggest the use of two-tailed unequal variance t-test instead of t-test if normality is not violated or the use of Wilcoxon rank-sum test if normality is violated.

We have added language to the manuscript to clarify that we will perform the normality and homoscedasticity tests. As described earlier, we used summary statistics estimated from the published data to perform power calculations. This limits what we can ascertain from the original data. We recalculated the samples sizes using a Welch’s t-test instead of a Student’s. The sample size we have planned is still sufficient. This is also true for the non-parametric Wilcoxon rank-sum test.

5) Although the reproducibility project is aimed toward reproducing previously published results, the reviewers would like for the authors to address the limitation of drawing conclusions for the use of only one cell line, A549. Specifically, activity of drugs in cell lines and xenografts is generally highly idiosyncratic. As a result, most journals require that any in vitro and in vivo experiments are replicated in multiple cell lines and in vivo models.

Thank you for providing this insight. We have added a sentence addressing this point to the Introduction. We will also include this point in the Discussion section of the Replication Study that will be published once the replication data has been generated. We agree the use of one model limits the overall conclusions that can be drawn. This project focuses on understanding if the effects drawn from a single model can be reproduced. While this does not speak to the robustness of the effect, such as can be inferred through multiple models/approaches, it does provide a mechanism to examine the extent to which an effect with a given model can be observed again. We will also limit the conclusions that can be drawn to only this model.