Natural products have an important role as prototypes in the synthesis of new anticancer drugs. Piperine is an alkaloid amide with antitumor activity and significant toxicity. Then, the N-(p-nitrophenyl)acetamide piperinoate (HE-02) was synthesized, and tested for toxicological and antitumor effects. The toxicity was evaluated in vitro (on RAW 264.7 cells and mice erythrocytes) and in vivo (acute toxicity in mice). The Ehrlich ascites carcinoma model was used to evaluate the antitumor activity of HE-02 (6.25, 12.5 or 25 mg/kg, intraperitoneally, i.p.), as well as toxicity. HE-02 induced only 5.01% of hemolysis, and reduced the viability of RAW 264.7 cells by 49.75% at 1000 µg/mL. LD50 (lethal dose 50%) was estimated at around 2000 mg/kg (i.p.). HE-02 reduced Ehrlich tumor cell viability and peritumoral microvessels density. There was an increase of Th1 helper T lymphocytes cytokine profile levels (IL-1β, TNF-α, IL-12) and a decrease of Th2 cytokine profile (IL-4, IL-10). Moreover, an increase was observed on reactive oxygen species and nitric oxide production. Weak in vivo toxicological effects were recorded. Our data provide evidence that the piperine analogue HE-02 present low toxicity, and its antitumor effect involves modulation of immune system to a cytotoxic Th1 profile.
Several phenotypes that impact the capacity of cancer cells to survive and proliferate are dynamic. Here we used the number of cells in colonies as an assessment of fitness and devised a novel method called Dynamic Fitness Analysis (DynaFit) to measure the dynamics in fitness over the course of colony formation. DynaFit is based on the variance in growth rate of a population of founder cells compared with the variance in growth rate of colonies with different sizes. DynaFit revealed that cell fitness in cancer cell lines, primary cancer cells, and fibroblasts under unhindered growth conditions is dynamic. Key cellular mechanisms such as ERK signaling and cell-cycle synchronization differed significantly among cells in colonies after 2 to 4 generations and became indistinguishable from randomly sampled cells regarding these features. In the presence of cytotoxic agents, colonies reduced their variance in growth rate when compared with their founder cell, indicating a dynamic nature in the capacity to survive and proliferate in the presence of a drug. This finding was supported by measurable differences in DNA damage and induction of senescence among cells of colonies. The presence of epigenetic modulators during the formation of colonies stabilized their fitness for at least four generations. Collectively, these results support the understanding that cancer cell fitness is dynamic and its modulation is a fundamental aspect to be considered in comprehending cancer cell biology and its response to therapeutic interventions.
Significance:
Cancer cell fitness is dynamic over the course of the formation of colonies. This dynamic behavior is mediated by asymmetric mitosis, ERK activity, cell-cycle duration, and DNA repair capacity in the absence or presence of a drug.
<p>Supplementary Fig. S5  Growth characteristics of individual treated colonies. a, GR of all colonies used in Fig. 1e. Mean {plus minus} SD in red. b. Pairwise correlations among the three GRs. c. Mean {plus minus} variance of GR2 of U251 glioma colonies according to the number of cells in CS1 or all colonies. Growth rate of colonies with 1 and 2 cells were different from all growth rates (ANOVA, Tukey's multiple comparison post-hoc test). d, Distribution of the number of colonies in each bin for Fig. 1e. e, GR of the indicated cell lines at high and low density. f, CVP and g, hypothesis plot of the glioma cell line A172wt (WT), GFP-tagged alone (GFP) or in the presence of 100 fold excess of untagged cells (GFP 1:100).</p>
<p>Supplementary Fig. S2 DynaFit implementations. a, data collected from Supplementary Fig. S1c is given as input to the two DynaFit apps. The Python bootstrap app and the R predictive modeling app are based on different analytical strategies. b, Colony Variance Plot formed by the Python app and c, its hypothesis plot. d, Colony Variance Plot formed by the R app and e, its hypothesis plot.</p>
<p>Supplementary Fig. S1 DynaFit principles. a, numerical representation of the rationale presented in Fig. 1a. Cells with static growth rates will produce colonies with the same variance in growth rate, regardless of colony size (upper scenario). Colonies containing cells with dynamic growth rates will present a lower variance of growth rates as they grow in number (lower scenario). b, schematic representation of the colony fitness experiment. Single cells are seeded, and colony fitness is quantified between two distinct time points. c, Example of experimental data obtained from b. For DynaFit, the colonies are grouped by their initial colony size. The growth rate variance is measured for each colony size.</p>
<p>Supplementary Fig. S7  Comparison of Dynafit results with growth rate. a, GR and DynaFit Cumulative Hypothesis plot results b. GR2 versus CS1-GR2 c. GR3 versus CS1-GR2 d. GR3 versus CS1-GR3 of untreated (green), treated only with cytotoxic drugs (red) and the combination of epigenetic modulators and cytotoxic drugs (blue).</p>
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