Unveiling the Synergistic Potential: Bispecific Antibodies in Conjunction with Chemotherapy for Advanced Non-Small-Cell Lung Cancer Treatment
Bibliographic record
Abstract
Lung cancer remains the leading cause of cancer-related mortality worldwide, with non-small-cell lung cancer (NSCLC) accounting for the majority of the cases. Despite advancements in targeted therapies and immunotherapies, many patients still rely on chemotherapy, highlighting the need for innovative treatment strategies. Bispecific antibodies (bsAbs), which feature two distinct binding sites capable of targeting different antigens, have emerged as a promising therapeutic approach, particularly in combination with chemotherapy. This review explores the scientific evolution and clinical application of bsAbs in NSCLC, focusing on their synergistic potential with chemotherapy. BsAbs, such as amivantamab, which targets EGFR and MET, have demonstrated significant efficacy in clinical trials, particularly in patients with EGFR mutations. The combination of bsAbs with chemotherapy enhances immune-mediated tumor destruction by modulating the tumor microenvironment and overcoming resistance mechanisms. Recent clinical trials have shown improved progression-free survival and overall survival when bsAbs such as amivantamab are combined with chemotherapy, underscoring their potential to transform NSCLC treatment. Many other clinical trials are underway that are evaluating newer bsAbs, such as ivonescimab, which targets PD1 and VEGF. This review also discusses ongoing clinical trials investigating various bsAbs targeting EGFR, PD-1, PD-L1, HER2, and other pathways, highlighting the future directions of bsAb-based therapies. As the field evolves, bsAbs are poised to become a cornerstone of multimodal NSCLC treatment, offering more effective and personalized therapeutic options for patients with advanced disease.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".