A Novel Proteomic Method Reveals NLS Tagging of T-DM1 Contravenes Classical Nuclear Transport in a Model of HER2-Positive Breast Cancer
Bibliographic record
Abstract
The next breakthrough for protein therapeutics is effective intracellular delivery and accumulation within target cells. Nuclear localization signal (NLS)-tagged therapeutics have been hindered by the lack of efficient nuclear localization due to endosome entrapment. Although development of strategies for tagging therapeutics with technologies capable of increased membrane penetration has resulted in proportional increased potency, nonspecific membrane penetration limits target specificity and, hence, widespread clinical success. There is a long-standing idea that nuclear localization of NLS-tagged agents occurs exclusively via classical nuclear transport. In the present study, we modified the antibody-drug conjugate trastuzumab-emtansine (T-DM1) with a classical NLS linked to cholic acid (cell accumulator [Accum]) that enables modified antibodies to escape endosome entrapment and increase nuclear localization efficiency without abrogating receptor targeting. In parallel, we developed a proteomics-based method to evaluate nuclear transport. Accum-modified T-DM1 significantly enhanced cytotoxic efficacy in the human epidermal growth factor receptor 2 (HER2)-positive SKBR3 breast cancer system. We discovered that efficacy was dependent on the nonclassical importin-7. Our evaluation reveals that when multiple classical NLS tagging occurs, cationic charge build-up as opposed to sequence dominates and becomes a substrate for importin-7. This study results in an effective target cell-specific NLS therapeutic and a general approach to guide future NLS-based development initiatives.
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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) | 0.000 | 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".