Tumor suppressor PLK2 may serve as a biomarker in triple-negative breast cancer for improved response to PLK1 therapeutics
Bibliographic record
Abstract
Abstract Polo-like kinase (PLK) family members play important roles in cell cycle regulation. The founding member PLK1 is oncogenic and preclinically validated as a cancer therapeutic target. Paradoxically, PLK2 (chromosome 5q11.2) is frequently deleted in human breast cancers, preferentially in basal-like and triple-negative breast cancer subtypes. Here, we found that PLK2 was tumor suppressive in breast cancer and knockdown of PLK1 rescued phenotypes induced by PLK2-loss both in vitro and in vivo . We also demonstrated that PLK2 directly interacted with PLK1 at prometaphase and that mutations in the kinase domain of PLK2, but not polo-box binding domains, changed their interaction pattern. Furthermore, treatment of syngeneic transplantation mouse tumor models and patient-derived xenografts using the PLK1 inhibitor volasertib alone, or in combination with carboplatin, indicated that PLK2-low breast tumors had a significantly better response to these drugs. Re-expression of PLK2 in an inducible PLK2-null mouse model reduced the therapeutic efficacy of volasertib. Taken together, our data suggest PLK2 loss may serve as a biomarker to predict response to PLK1 therapeutics, alone and in combination with chemotherapy. Significance The tumor suppressive role of PLK2, and its relationship with the oncogene PLK1, provide a mechanistic rationalization to use PLK1 inhibitors in combination with chemotherapy to treat PLK2 low/deleted tumors. TNBC, and other cancers with low PLK2 expression, are such candidates to leverage precision medicine to identify patients who might benefit from treatment with these inhibitors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".