A nucleolytic lupus autoantibody is toxic to BRCA2-deficient cancer cells
Bibliographic record
Abstract
Cancer cells with defects in DNA repair are highly susceptible to DNA-damaging agents, but delivery of therapeutic agents into cell nuclei can be challenging. A subset of lupus autoantibodies is associated with nucleolytic activity, and some of these antibodies are capable of nuclear penetration. We hypothesized that such antibodies might have potential as therapeutic agents targeted towards DNA repair-deficient malignancies. We identified the lupus autoantibody 5C6 as a cell-penetrating nucleolytic antibody and found that 5C6 has a differential effect on a matched pair of BRCA2-proficient and deficient DLD1 colon cancer cells. 5C6 selectively induced γH2AX in, and suppressed the growth of, the BRCA2-deficient cells. These findings demonstrate the potential utility of 5C6 in targeted therapy for DNA repair-deficient malignancies and strengthen the rationale for studies of additional lupus autoantibodies in order to identify the best candidates for development as therapeutic agents. In addition, the toxic effect of 5C6 on BRCA2-deficient cells provides further support for the hypothesis that some lupus autoantibodies contribute to the lower risk of specific cancers associated with systemic lupus erythematosus.
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 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 | 0.000 |
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".