<scp>HMGA</scp>2 as a functional antagonist of <scp>PARP</scp>1 inhibitors in tumor cells
Bibliographic record
Abstract
Poly(ADP-ribose) polymerase 1 inhibitors alone or in combination with DNA damaging agents are promising clinical drugs in the treatment of cancer. However, there is a need to understand the molecular mechanisms of resistance to PARP1 inhibitors. Expression of HMGA2 in cancer is associated with poor prognosis for patients. Here, we investigated the novel relationship between HMGA2 and PARP1 in DNA damage-induced PARP1 activity. We used human triple-negative breast cancer and fibrosarcoma cell lines to demonstrate that HMGA2 colocalizes and interacts with PARP1. High cellular HMGA2 levels correlated with increased DNA damage-induced PARP1 activity, which was dependent on functional DNA-binding AT-hook domains of HMGA2. HMGA2 inhibited PARP1 trapping to DNA and counteracted the cytotoxic effect of PARP inhibitors. Consequently, HMGA2 decreased caspase 3/7 induction and increased cell survival upon treatment with the alkylating methyl methanesulfonate alone or in combination with the PARP inhibitor AZD2281 (olaparib). HMGA2 increased mitochondrial oxygen consumption rate and spare respiratory capacity and increased NAMPT levels, suggesting metabolic support for enhanced PARP1 activity upon DNA damage. Our data showed that expression of HMGA2 in cancer cells reduces sensitivity to PARP inhibitors and suggests that targeting HMGA2 in combination with PARP inhibition may be a promising new therapeutic approach.
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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) | 0.002 | 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".