Methodology

Comparison of the expression of EGFR and HER2 in normal pancreas tissues and pancreatic cancer tissues. Transcriptomic gene expression data of normal pancreas tissues were retrieved from the GTEX database. Transcriptomic expression data of pancreatic cancer were obtained from The Cancer Genome Atlas. The mRNA sequencing data mapping and quantification were performed using the same protocol to overcome potential batch effects. The expression of EGFR and HER2 (i.e., ERBB2) in normal and tumoral tissues were then compared using the Wilcoxon rank-sum test.

Correlation of EGFR and HER2 expression with the overall survival of pancreatic cancer patients. The patients were divided into two groups based on their EGFR expression, using the median values as the cutoff. The overall survival of the EGFR-high and EGFR-low groups was then compared using the log-rank test. Similarly, patients were divided into HER2-high and HER2-low groups, and their overall survival was compared. Finally, the patients were divided into four groups, based on their grouping based on EGFR and HER2. The overall survival of the four groups was then compared using log-rank test.

Comparison of EGFR and HER2 expression in parental and gemcitabine-resistant pancreas cancer cells. Two pancreatic cancer cell lines, namely BxPC-3 and CFPAC-1, were treated with gemcitabine until they developed resistance. The transcriptomes of both the parental cells and the resistant cells were then profiled by mRNA sequencing. We obtained the data from GEO (GSE140077) and then compared the expression of EGFR and HER2 by t-test.

Comparison of EGFR and HER2 expression in gemcitabine responder and non-responder pancreatic cancer patients. We first selected pancreatic cancer patients who received gemcitabine treatment in the TCGA database and then filtered out those whose response information was not available. Patients who achieved complete remission, partial response, or stable disease were classified as responders, while those who suffered progressive disease after gemcitabine treatment were classified as non-responders. We then compared the mRNA expression of EGFR and HER2 in the tumors of the responders and non-responders using Wilcoxon’s rank-sum test.

Results

EGFR and HER2 are both overexpressed in pancreatic tumors (n = 179) compared to normal pancreas (n = 171) with high significance (P = 4.49e-22 and 6.44e-43 for EGFR and HER2, respectively, Fig. 2a-1b).

In pancreatic adenocarcinoma patients, high expression of EGFR was associated with significantly worse overall survival (P = 0.0012, Fig. 3a). Similarly, high expression of HER2 was also associated with worse overall survival (P = 0.026, Fig. 3b). Notably, EGFR and HER2 overexpression were observed in different patients. Patients with either EGFR or HER2 high expression were associated with worse survival (P = 0.00012, Fig. 3c).

To understand the role of EGFR and HER2 in pancreatic cancer gemcitabine resistance, we first compared their expression in the parental and gemcitabine-resistant cells in two pancreatic cancer cell lines, BxPC-3 and CFPAC-1. The results showed that in BxPC-3, HER2 was significantly upregulated after developing gemcitabine resistance (P = 0.013, Fig 4a), but EGFR had no significant change. Conversely, in CFPAC-1, the gemcitabine resistant cells had significantly higher expression of EGFR (P = 0.0069, Fig 4a) than the parental cells, but HER2 expression had no difference. We then analyzed the pancreatic cancer patients’ data. Among the 107 patients in the TCGA pancreatic adenocarcinoma cohort who received gemcitabine treatment, 30 had no response and experienced progressive disease, while 24 achieved complete remission, 1 achieved partial response and 5 achieved stable disease. Comparing the expression levels of EGFR plus HER2, the non-responders had significantly higher expression than the responders (P = 0.04, Fig. 4b)

Conclusion

In summary, the bioinformatics analysis showed that EGFR and HER2 are both upregulated in pancreatic cancers, and overexpression of these two genes was associated with a worse patient prognosis. We further showed that overexpression of EGFR and HER2 was associated with gemcitabine resistance in pancreatic cancer cell lines and patients. These results provide the rationale for targeting both EGFR and HER2 to improve pancreatic cancer treatment and overcome gemcitabine resistance.

Following the first dry lab experiment, we have determined that EGFR and HER2 are the dual targets for our Biobrick design. To achieve this, we have developed peptide sequences targeting these two protein receptors for Panobody production. Before progressing to this phase, we first used Alphafold to create a three-dimensional model of Panobody. We then employed a molecular docking method to confirm its specificity for EGFR2 and HER2, comparing the results to a scramble control. The analysis is comprised of two distinct sections

Methodology

Prediction of 3D structure of Panobody using Alphafold

The 3D structures of Panobody and Scramble 6 were predicted using the AlphaFold3 web server, generating five distinct models (0, 1, 2, 3, and 4) for each protein based on their 3D conformations. These models were subsequently downloaded in the PDB format.

Each predicted structure was uploaded to the SAVES v6.1 server for structural validation. The models were evaluated using three key tools: ERRAT (> 90%), VERIFY3D (requiring a pass), and PROCHECK (with > 90% of residues in the most favored regions of the Ramachandran plot).

The best-performing models (0 and 2) for both peptides, highlighted in red (Table 1 and 2), were selected and uploaded to the Protein Preparation Wizard in Maestro (Schrödinger) for refinement using default settings.

The crystal structures of HER2 and EGFR (PDB IDs: 3PP0 and 1XKK, respectively) were retrieved from the Protein Data Bank in PDB format. These structures were then processed in the Protein Preparation Wizard of Maestro (Schrödinger), where heteroatoms (such as water molecules and other ligands) were removed, and polar hydrogens were added.

Molecular docking was performed using the BioLuminate module in Maestro (Schrödinger). In this process, the prepared HER2 and EGFR structures were uploaded as receptors and the prepared peptide models were uploaded as ligands. All docking parameters were set to default values. Additionally, attraction restraints were applied, with a bonus value of 0.51, to specific residues of each peptide during docking with their respective receptors. Following the docking procedure, the resulting complexes were analyzed for molecular interactions using the Protein Interaction Analysis module within the Schrödinger Biologics suite.

Results

The molecular docking analysis of Panobody and Scramble 6 revealed distinct binding patterns and interaction profiles with the HER2 and EGFR receptors, highlighting Panobody's superior binding affinity and stability.

For the Panobody, the HER2 nanobody demonstrated a robust interaction network through its key residues, notably His5, Asn37, Leu54, Arg57, Glu68, and Glu109 with the HER2 receptor (Fig. 6a). These residues establish strong hydrogen bonds that are crucial for stabilizing the peptide-receptor complex. Additional interactions (salt bridge) with Glu51 further contribute to binding integrity, forming a well-anchored complex. This extensive network of electrostatic interactions underscores the stability and specificity of Panobody in targeting HER2, indicating its potential as a highly effective inhibitor of HER2-mediated pathways.

Similarly, the Panobody’s EGFR nanobody displays a strong interaction profile with the EGFR receptor through its critical residues, including Asp190, Thr192, Tyr194, Asp196, Phe202, Thr203, and Trp238, which form essential hydrogen bonds and electrostatic interactions. The presence of π–π stacking interactions, particularly involving the key residue (Tyr194), further enhanced the molecular docking strength, contributing to the robust binding stability of the Panobody-EGFR complex (Fig. 6b). The observed interactions, including additional stabilizing forces from hydrophobic residues, indicate a well-organized and stable binding mode that can effectively modulate the EGFR signaling pathways.

In comparison, Scramble 6, which showed noteworthy interactions, presented a less extensive and intricate binding network. The HER2 nanobody of Scramble 6 interacts with significant residues of the HER2 receptor, such as Lys765, Thr759, and Ash838, forming key hydrogen bonds and salt bridges and π-π stacking interactions, with Tyr835 playing a stabilizing role (Fig. 7a). However, the overall interaction network was less comprehensive than that observed for the Panobody. Similarly, Scramble 6 forms crucial hydrogen bonds and salt-bridge interactions with key residues (Lys739, Val769, Asp770, Hie773, Lys823, and Thr847) of the EGFR receptor (Fig. 7b); however, the binding appears less reinforced, as hydrogen bonds are not as widespread or integrative as in the Panobody-EGFR complex.

Conclusion

Upon thorough analysis, it is evident that the Panobody exhibits a more robust and stable binding profile with both HER2 and EGFR receptors, owing to its extensive network of hydrogen bonds, electrostatic interactions, and π-π stacking interactions. The higher number of critical interactions and the comprehensive involvement of key residues suggest that Panobody has superior affinity and stability in targeting both HER2 and EGFR. This makes it a more potent candidate for dual-target therapeutic applications, in contrast to Scramble 6, which, while effective, demonstrates a comparatively weaker interaction network. The intricate binding mechanisms of Panobody provide a strong foundation for its potential use in therapeutic interventions for HER2- and EGFR-driven diseases.