Abstract CT078: Phase 1 study of SGN-EGFRd2 in solid tumors (SGNEGFRd2-001)
Bibliographic record
Abstract
Abstract Background: Epidermal growth factor receptor (EGFR) is a cell surface receptor expressed in a variety of solid tumors. Gamma delta (γδ)-T cells represent a conserved T cell subset that can induce cell death in a wide range of malignancies. The presence of γ9δ2-T cells in solid tumors strongly correlates with better patient survival (Gentles 2015; Tosolini 2017). In addition to direct, potent killing by release of cytotoxic molecules, activated γ9δ2-T cells process and present antigen and contribute to a cascade response resulting in downstream activation of innate and adaptive immune cells to mediate further tumor cell killing (Brandes 2005; Devilder 2006; Vantourout 2013). Novel approaches that improve targeting and activation of γ9δ2-T cells may lead to the development of effective and safe cancer treatments. SGN-EGFRd2 is an investigational, bispecific humanized heavy chain-only antibody (HcAb). Advantages of HcAbs include their small size, high solubility, high stability, and excellent tissue penetration in vivo (Bannas 2017). SGN-EGFRd2 simultaneously binds EGFR and the γ9δ2-T cell receptor, resulting in the conditional activation of γ9δ2-T cells. Importantly, full activation of γ9δ2-T cells requires a secondary phosphoantigen stress signal present only in malignant and certain infected cells (Deseke 2020; Herrmann 2020; Rigau 2020; Vantourout 2013), but not normal healthy cells, conferring preferential tumor-directed cytotoxicity. The proposed cytotoxic mechanism of action is independent of KRAS and BRAF mutations (King 2023) known to confer resistance to approved anti-EGFR therapies in colorectal cancer (CRC). This first-in-human study is evaluating the safety, tolerability, and antitumor activity of SGN-EGFRd2 in patients with advanced solid tumors. Methods: SGNEGFRd2-001 (NCT05983133) is an open-label, multicenter, phase 1, dose escalation and expansion study evaluating SGN-EGFRd2 monotherapy in patients with relapsed/refractory (R/R) CRC, head and neck squamous cell cancer, non-small cell lung cancer, and pancreatic ductal adenocarcinoma in 3 parts: dose escalation (Part A; n ~ 75), dose optimization (Part B; optional; up to n = 40), and dose expansion (Part C; up to n = 160). Patients must have R/R disease, or be intolerant to standard of care therapies, with no appropriate standard therapy available. Patients must have measurable disease per RECIST v1.1, an ECOG performance status of 0 or 1, and adequate organ function. Patients are eligible regardless of tumor EGFR expression or EGFR/KRAS/BRAF mutation status. Patients will receive SGN-EGFRd2 as an intravenous infusion. Primary study objectives are safety and tolerability, maximum tolerated dose, and recommended dose and schedule. Secondary objectives include pharmacokinetics, immunogenicity, and antitumor activity. Tumor samples will be analyzed for exploratory biomarkers. Accrual is ongoing in the US. Additional sites in Europe and Canada are planned. Citation Format: Hirva Mamdani, Joel Randolph Hecht, Rachel E. Sanborn, Maria Corinna Palanca-Wessels, Mingjin Yan, Martin Gutierrez, David L. Bajor. Phase 1 study of SGN-EGFRd2 in solid tumors (SGNEGFRd2-001) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr CT078.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".