Protein Engineering Paving the Way for Next-Generation Therapies in Cancer
Bibliographic record
Abstract
Cancer continues to be a leading cause of global mortality, necessitating innovative therapeutic strategies to address its complexity and heterogeneity. Protein engineering has emerged as a transformative approach in developing cancer biotherapeutics, enabling the creation of highly specific, potent, and adaptable treatments. This paper provides a comprehensive review of the state-of-the-art in protein engineering, highlighting key techniques such as directed evolution, rational design, and hybrid approaches that underpin the development of monoclonal antibodies, bispecific antibodies, and novel fusion proteins. Case studies of FDA-approved therapies, including engineered monoclonal antibodies like trastuzumab and bispecific T-cell engagers such as blinatumomab, are discussed to illustrate the impact of these advancements. Furthermore, emerging trends, including AI-driven protein design and synthetic biology applications, are explored alongside their potential to revolutionize future cancer treatments. Challenges such as immunogenicity, stability, and scalability are critically evaluated, offering insights into potential solutions and future research directions. By synthesizing advancements in protein science and oncology, this paper aims to guide researchers and clinicians in harnessing the full potential of engineered proteins for cancer therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| 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 teacher head, 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".