Functional genomic characterization of a synthetic anti-HER3 antibody reveals a role for ubiquitination by RNF41 in the anti-proliferative response
Bibliographic record
Abstract
Dysregulation of the ErbB family of receptor tyrosine kinases is involved in the progression of many cancers. Antibodies targeting the dimerization domains of family members EGFR and HER2 are approved cancer therapeutics, but efficacy is restricted to a subset of tumors and resistance often develops in response to treatment. A third family member, HER3, heterodimerizes with both EGFR and HER2 and has also been implicated in cancer. Consequently, there is strong interest in developing antibodies that target HER3, but to date, no therapeutics have been approved. To aid the development of anti-HER3 antibodies as cancer therapeutics, we combined antibody engineering and functional genomics screens to identify putative mechanisms of resistance or synthetic lethality with antibody-mediated anti-proliferative effects. We developed a synthetic antibody called IgG 95, which binds to HER3 and promotes ubiquitination, internalization, and receptor down-regulation. Using an shRNA library targeting enzymes in the ubiquitin proteasome system, we screened for genes that effect response to IgG 95 and uncovered the E3 ubiquitin ligase RNF41 as a driver of IgG 95 anti-proliferative activity. RNF41 has been shown previously to regulate HER3 levels under normal conditions and we now show that it is also responsible for down-regulation of HER3 upon treatment with IgG 95. Moreover, our findings suggest that down-regulation of RNF41 itself may be a mechanism for acquired resistance to treatment with IgG 95 and perhaps other anti-HER3 antibodies. Our work deepens our understanding of HER3 signaling by uncovering the mechanistic basis for the anti-proliferative effects of potential anti-HER3 antibody therapeutics.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".