A subset of CB002 xanthine analogs bypass p53-signaling to restore a p53 transcriptome and target an S-phase cell cycle checkpoint in tumors with mutated-p53

Mutations in TP53 occur commonly in the majority of human tumors and confer aggressive tumor phenotypes, including metastasis and therapy resistance. CB002 and structural-analogs restore p53 signaling in tumors with mutant-p53 but we find that unlike other xanthines such as caffeine, pentoxifylline, and theophylline, they do not deregulate the G2 checkpoint. Novel CB002-analogs induce pro-apoptotic Noxa protein in an ATF3/4-dependent manner, whereas caffeine, pentoxifylline, and theophylline do not. By contrast to caffeine, CB002-analogs target an S-phase checkpoint associated with increased p-RPA/RPA2, p-ATR, decreased Cyclin A, p-histone H3 expression, and downregulation of essential proteins in DNA-synthesis and DNA-repair. CB002-analog #4 enhances cell death, and decreases Ki-67 in patient-derived tumor-organoids without toxicity to normal human cells. Preliminary in vivo studies demonstrate anti-tumor efficacy in mice. Thus, a novel class of anti-cancer drugs shows the activation of p53 pathway signaling in tumors with mutated p53, and targets an S-phase checkpoint.


Sample-size estimation
• You should state whether an appropriate sample size was computed when the study was being designed • You should state the statistical method of sample size computation and any required assumptions • If no explicit power analysis was used, you should describe how you decided what sample (replicate) size (number) to use Please outline where this information can be found within the submission (e.g., sections or figure legends), or explain why this information doesn't apply to your submission: In vivo experiment sample size was determined as n=10 mice per cohort. Tumor volume experiments were compared at a fixed time point between analogue #4 treatment and DMSO vehicle controls. Sample size calculation was determined as follows using the approximation for tumor volume and solving the equations in order to achieve statistical power between treatment and control: z(0.8)*sqrt[(s(c)^2+ s(e)^2)/n] -z(0.06)*s(c)*sqrt(2/n) = mu(c) -mu(e) where z(0.8) = 0.842, z(0.06) = -1.555 are the quantities associated with 80% power and 6% type I error, n = number animals per group, mu(e) is the postulated mean volume among experimental group and mu(c) is that of the control animals, and s(c) and s(e) are standard deviations of control volumes and experimental volumes, respectively. Substituting n = 10, mu(e) = mu(c)/2 results in the requirement that: z(0.8)*sqrt[(s(c)^2+ s(e)^2)/n] -z(0.06)*s(c)*sqrt(2/n) be less than or equal to mu(c) -mu(e). Tumor volumes are bounded below by zero so we approximated the two standard deviations by s(c) = mu(c)/2 and s(e) = mu(e)/2. Making these substitutions plus setting mu(e) = mu(c)/2 results in the requirement that z(.8)*sqrt(1.25/n)-z(0.06)*sqrt(2/n) be less than or equal to 1. With n=10 the above quantity equals 0.9929 which justifies that 10 animals per group result in 80% power and 6% type I error.
Experimental information can be found in the materials and methods section as well as the Figure 6 legend.

Replicates
• You should report how often each experiment was performed • You should include a definition of biological versus technical replication • The data obtained should be provided and sufficient information should be provided to indicate the number of independent biological and/or technical replicates • If you encountered any outliers, you should describe how these were handled • Criteria for exclusion/inclusion of data should be clearly stated • High-throughput sequence data should be uploaded before submission, with a private link for reviewers provided (these are available from both GEO and ArrayExpress) Please outline where this information can be found within the submission (e.g., sections or figure legends), or explain why this information doesn't apply to your submission: The number of biological replicates can be found in the materials and methods section, Figure legends or supplemental files description when applicable.

Statistical reporting
• Statistical analysis methods should be described and justified • Raw data should be presented in figures whenever informative to do so (typically when N per group is less than 10) • For each experiment, you should identify the statistical tests used, exact values of N, definitions of center, methods of multiple test correction, and dispersion and precision measures (e.g., mean, median, SD, SEM, confidence intervals; and, for the major substantive results, a measure of effect size (e.g., Pearson's r, Cohen's d) • Report exact p-values wherever possible alongside the summary statistics and 95% confidence intervals. These should be reported for all key questions and not only when the p-value is less than 0.05.
Please outline where this information can be found within the submission (e.g., sections or figure legends), or explain why this information doesn't apply to your submission: (For large datasets, or papers with a very large number of statistical tests, you may upload a single table file with tests, Ns, etc., with reference to sections in the manuscript.)

Group allocation
• Indicate how samples were allocated into experimental groups (in the case of clinical studies, please specify allocation to treatment method); if randomization was used, please also state if restricted randomization was applied • Indicate if masking was used during group allocation, data collection and/or data analysis