(J-382) The growing trend of colorectal cancer during pregnancy: A computational study of prolactin signaling and cancer aggressiveness

Introduction:: The female body undergoes a tremendous amount of change during pregnancy, yet the underlying consequences of these adaptations on the pathology of colorectal cancer (CRC) is largely unknown. During pregnancy, one in 13,000 women is diagnosed with CRC, a number that is predicted to increase [1]. At the time of diagnosis, 73.2% of CRC cases in pregnant women have reached an advanced stage, whereas 50% of non-pregnant-women cases have reached an advanced stage. Metastases are identified in approximately 48% of pregnant CRC patients at the time of diagnosis. One study compiled 119 pregnant patient case reports and found the median maternal survival to be 36 months post diagnosis. The high morbidity of pregnant CRC patients demonstrates the need for increased understanding of how pregnancy can impact CRC progression. During pregnancy, there is a significant increase in serum prolactin (PRL) levels, especially in the trimester. PRL plays a large role in the lactation and reproduction processes of mammals. PRL causes physiological changes by activating signaling pathways when it binds to the prolactin receptor (PRLR). The principal signaling pathway activated by PRL-PRLR interactions is the janus kinase (JAK) – signal transducer and activator of transcription (STAT) pathway, specifically the JAK2/STAT3 pathway. Opportunistic cancer cells take advantage of aberrant JAK/STAT signaling to increase oncogenic activity via increased epithelial-to-mesenchymal transition and stem-like characteristics, leading to poor patient prognosis. In this study a computational modeling approach was used to further understand the effect of increased PRL serum levels during pregnancy on CRC tumors.


Materials and Methods:: The computational model is based on a two-cell approach, where “cell 1” calculates the activation of the JAK2/STAT3 pathway by PRL, resulting in the upregulation of JAG1 on its surface (Figure 1A). The input to CELL_1 is the fluctuations over time of PRL serum levels in pregnant women from the study by Biswas et al summarized in Figure 1B [2]. The second cell in the simulation models the hyper activation of the NOTCH1 pathway after increased binding with JAG1 receptors on neighboring cells, leading to the upregulation of stemness and EMT markers. The endpoint JAG1 expression calculated for CELL_1 is used as the input for CELL_2. The CELL_1 algorithm was developed in MATLAB based on the models by Mortlock et al and Yamada et al [3], [4]. This section of the model follows the canonical JAK/STAT activation pathway (Figure 1C). Initial fitting of the CELL_1 model parameters (reaction kinetics) was done using the experimental results published by Neradugomma et al [5]. The model was run using 500ng/mL of serum PRL as the initial condition and the parameter initial values were fit manually to the experimental results following the guidelines outlined in Mortlock et al. Once the initial fit was completed, the model was run ten times using the PRL serum levels outlined in Figure 1B. These initial values include the PRL serum levels observed every 8 weeks from the 8^th to the 36^th week of gestation, in addition to the overall minimum and maximum values measured.

Results, Conclusions, and Discussions:: The model was successfully built in MATLAB to simulate the activation of the JAK2/STAT3 pathway resulting in JAG1 overexpression due to varying PRL concentrations. This started with the binding of JAK2 (Figure 1C R1) to the intracellular domain of the PRLR. PRL binds to the PRLR-JAK complex (Figure 1C R2-4). Inactive STAT3 binds to the PRL-PRLR-JAK2 cluster (Figure 1C R5), is phosphorylated (pSTAT3) (Figure 1C R6) and forms a dimer in the cytoplasm (Figure 1C R8). The dimer passes through a nuclear pore to bind with the DNA (Figure 1C R14) and acts as a transcription factor for JAG1. Lastly, JAG1 mRNA is transcribed and translated into a protein expressed on the cell membrane (Figure 1C R30-34). Three regulatory modules were implemented: dephosphorylation by the PPX/PPN (Figure 1C R11-17), the SOCS, (Figure 1C R18-24), and the SHP2 modules (Fig. 1C R9&10).

Results with the initial fitting for the relative pSTAT3 (Figure 1D) and JAG1 mRNA (Figure 1E) expression were plotted against the experimental results from the literature. STAT3 phosphorylation reached a maximum at the 1.5hr mark, in our model and then the stabilized at 4hrs after treatment. While the computational model generated curves in both cases that closely resembled the experimental values, further model fitting needs to be performed. Both pSTAT3 (Figure 1F) and JAG1 (Figure 1G) modeled expressions were sensitive to the incremental changes in initial PRL expression. The traditional STAT3 phosphorylation peak and stabilization were observed in initial PRL concentrations over 47ng/mL (week 20). This result mirrors the output data from the JAK/STAT1 model by Yamada et al. In comparison, JAG1 expression increased over time in all groups. The maximum modeled concentrations of each protein were plotted over time. Both pSTAT3 (Figure 1H) and JAG1 (Figure 1I) maximum concentrations increased linearly each week with increasing initial PRL serum levels. We successfully constructed a computational model recapitulating the activation of the JAK/STAT pathway in response to pregnancy serum levels of PRL. Thus, we can better understand how changes in women’s bodies during pregnancy can result in increased CRC aggressiveness, a problem currently not addressed in the literature.


Acknowledgements (Optional): :

References (Optional): : [1] G. Pellino et al., “Colorectal cancer diagnosed during pregnancy: systematic review and treatment pathways,” European Journal of Gastroenterology & Hepatology, vol. 29, no. 7, pp. 743–753, Jul. 2017, doi: 10.1097/MEG.0000000000000863.

[2] S. Biswas and C. H. Rodeck, “Plasma prolactin levels during pregnancy,” Br J Obstet Gynaecol, vol. 83, no. 9, pp. 683–687, Sep. 1976, doi: 10.1111/j.1471-0528.1976.tb00913.x.

[3] R. D. Mortlock, S. K. Georgia, and S. D. Finley, “Dynamic Regulation of JAK-STAT Signaling Through the Prolactin Receptor Predicted by Computational Modeling,” Cel. Mol. Bioeng., Sep. 2020, doi: 10.1007/s12195-020-00647-8.

[4] S. Yamada, S. Shiono, A. Joo, and A. Yoshimura, “Control mechanism of JAK/STAT signal transduction pathway,” FEBS Letters, vol. 534, no. 1–3, pp. 190–196, 2003, doi: 10.1016/S0014-5793(02)03842-5.

[5] N. K. Neradugomma et al., “Prolactin signaling enhances colon cancer stemness by modulating Notch signaling in a Jak2-STAT3/ERK manner,” Carcinogenesis, vol. 35, no. 4, pp. 795–806, Apr. 2014, doi: 10.1093/carcin/bgt379.