High-Throughput Molecular Cancer Cell Line Characterization Using Digital Multiplex Ligation-Dependent Probe Amplification for Improved Standardization of in Vitro Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Tumor cell lines are widely used for cancer research, but challenges regarding quality control of cell line identity, cross contamination, and tumor somatic molecular stability remain, demanding novel approaches beyond conventional short tandem repeat profiling. A total of 21 commonly used multiple myeloma cell lines obtained from public repositories were analyzed by digital multiplex ligation-dependent probe amplification (digitalMLPA) to characterize germline single-nucleotide polymorphisms, insertions/deletions, and somatic copy number aberrations (CNAs). Using generated profiles and an in-house developed analytical pipeline, blinded experiments were performed to determine capability of digitalMLPA to predict cell line identity and potential spike-in DNA contamination in 41 anonymized cell line samples. The dominant cell line was correctly identified in all cases, and cross contamination was correctly detected in 33 of 37 samples with spike-in DNA; there were no false-positive predictions. The four samples in which spike in was not detected all carried low levels of contamination (1%), whereas levels of contamination ≥5% were correctly identified in all cases. Unsupervised clustering of CNA profiles identified shared commonalities that correlated with initiating Ig heavy locus translocation events. Longitudinal CNA assessment of nine cell lines revealed changes under standard culturing conditions not detected by insertion/deletion profiling alone. Results suggest that digitalMLPA can be utilized as a high-throughput tool for advanced quality assurance for in vitro cancer research.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 it