Engineered Antibodies for Igsf8 and Tgfbr1 Modulate TGF-β Signals in Melanoma
Bibliographic record
Abstract
Comprehensive understanding of molecular pathways will identify the mechanisms underpinning disease. Including cancer progression. TGF-β signals can behave opposingly depending on context, yet key molecular insights of this regulation are still lacking. It is increasingly recognized that functional effects of this pathway are regulated via protein interactions and modifications of peripheral, accessory proteins, in addition to the core TGF-β proteins. Clarifying the contextual regulation of TGF-β signals would support the innovation of life-changing therapies that target TGF-β, given the importance of this pathway in nearly all cancers. Metastatic melanoma is the most aggressive skin cancer, and its invasive progression is primarily driven by TGF-β signals. Recent evidence demonstrates that Igsf8 can negatively regulate TGF-β signals in melanoma, suggesting that Igsf8 may hold a regulatory role for TGF-β signaling that is specific to melanoma. Efforts to target TGF-β in cancer include small molecules, antisense oligonucleotides, and monoclonal antibodies. Antibodies offer numerous advantages including increased target specificity, dose-dependence and modular structure that enables virtually limitless modification via protein engineering methods. Here, I have developed a method to engineer synthetic antibodies targeting multiple protein domains without structural information, using a phage-displayed antibody library. I then characterize antibody specificity, affinity, stability and epitope bins and screen for cell-binding. The cumulation of this data was then used to identify the most promising antibody candidates for Igsf8 with the goal to target the multiple domains of this protein. I then assessed the functional effects of Igsf8 antibody treatment on melanoma cells by setting up a highly sensitive cell signaling assay that detected any TGF-β signaling changes in vitro using a highly metastatic cell line. This assay demonstrated that antibodies binding to the membrane-proximal domain of Igsf8 could influence TGF-β signals, identifying a domain-specific function of Igsf8 in its regulation of TGF-β signaling in melanoma. My work supports a versatile method to develop domain-specific antibodies that can be applied to any member of the proteome, provides the field of Igsf8 research with specific protein tools for this target and advances our current understanding of melanoma by exploring a novel avenue for potentially new melanoma treatments.
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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) | 0.001 | 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".