Abstract LB-114: GC1118, a new anti-EGFR antibody overcome acquired resistance to cetuximab in colorectal cancer xenograft model
Bibliographic record
Abstract
Abstract Purpose: Anti-epidermal growth factor receptor (EGFR) antibodies such as cetuximab and panitumumab are widely used to treat patients with metastatic colorectal cancer (mCRC). However, patients eventually develop resistance to these drugs, which is one of the biggest challenges of EGFR-targeted therapy. Retrospective clinical studies revealed that mCRC patients with higher expression of low-affinity EGFR ligands such as amphiregulin and epiregulin had longer median progression free survival. On the contrary, patients refractory to cetuximab tended to have higher level of TGF-α expression according to the gene expression analysis. Recently, we demonstrated that GC1118 is a prominent blocker of both high- and low-affinity EGFR ligands induced signaling while cetuximab is limited to low-affinity ligands. In this study, we generated and characterized cetuximab-resistant CRC cell line and tested anti-tumor efficacy of GC1118 on cetuximab-resistant CRC tumor models. Experimental Design: To develop cetuximab-resistant SW48 colon cancer cell line (SW48CR) in vivo, mice bearing SW48 xenograft colorectal tumors were continuously exposed to cetuximab for several months. K-Ras mutation was analyzed by PCR and EGFR ligand expression was determined by ELISA assay. Results: First, we determined K-Ras mutation status and EGFR expression to characterize SW48CR cell line. K-Ras mutation is one of the most well-known cetuximab resistance mechanisms in CRC. There was no difference in EGFR expression level between SW48 and SW48CR. K-Ras expression was slightly increased and constitutively activated in SW48CR without mutation. Cetuximab resistance was confirmed by measuring proliferation and EGFR signaling inhibition by cetuximab in vitro. As expected, cetuximab was incapable of inhibiting cell proliferation and EGFR downstream signals in SW48CR cell line. Using SW48CR xenograft mouse model, we found that GC1118 suppressed SW48CR tumor growth by 80% while cetuximab inhibited less than 35%. Despite of this potent anti-tumor efficacy of GC1118 in vivo, GC1118 had no effect on SW48CR proliferation in vitro. This indicated GC1118 anti-tumor effect on SW48CR may be related to tumor microenvironment. In addition, EGFR ligand expression analysis indicated that HB-EGF, one of the high-affinity EGFR ligands was significantly increased in SW48CR compared with SW48, as previously reported by others. Since high-affinity EGFR ligands such as HB-EGF and TGF-α were implicated in tumor microenvironment, we are currently investigating GC1118 effects on tumor vasculature of SW48CR xenograft by immunohistochemical staining. Conclusion: These results suggest that overexpression of HB-EGF may contribute to cetuximab resistant CRC and GC1118 could be rational strategy to treat patients who acquired cetuximab resistance. Citation Format: Shi-Nai Lee, Hyun-Jung Cho, Yangmi Lim, Minkyu Hur, Eun Hee Lee, Jae-Chul Lee, Kyuhyun Lee, Sujeong Kim, Jonghwa Won. GC1118, a new anti-EGFR antibody overcome acquired resistance to cetuximab in colorectal cancer xenograft model. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr LB-114.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".