Targeting breast cancer exosomes with nucleic aptamers: innovative tools for early diagnosis and therapy
Bibliographic record
Abstract
Exosomes are emerging as promising target for early diagnosis and therapy in diff erent oncological conditions including breast cancer (BC). However, the development of tools able to easily and specifi cally target cancer cell-derived exosomes still represent a fundamental issue that is required to realize their clinical utility. Nucleic-acid aptamers are a promising class of structured single stranded oligonucleotides that serve as high affi nity ligands of disease-associated proteins. Given their high potential in diagnosis and therapy, we addressed the development of aptamers specifi c for BC-derived exosomes. To this end, we developed a novel SELEX strategy by using exosomes purifi ed from primary BC cells as positive selection target. By such a strategy we isolated nuclease resistant RNA aptamers able to specifi cally discriminate BC-derived exosomes from those produced by normal cells. Th e best sequences were optimized identifying short molecules (about 30-35 mer) that was characterized as tools for exosome detection. Further, we demonstrated that the developed aptamers inhibited exosome cellular uptake antagonizing cancer exosome-induced cell migration. By proteomic approach we identifi ed possible targets that we are characterizing. Our results underline the great potential of isolated aptamers as tools for the development of innovative strategies for BC early diagnosis and therapy.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".