Genomic and gene expression profiling of minute alterations of chromosome arm 1p in small-cell lung carcinoma cells
Bibliographic record
Abstract
Genetic alterations occurring on human chromosome arm 1p are common in many types of cancer including lung, breast, neuroblastoma, pheochromocytoma, and colorectal. The identification of tumour suppressors and oncogenes on this arm has been limited by the low resolution of current technologies for fine mapping. In order to identify genetic alterations on 1p in small-cell lung carcinoma, we developed a new resource for fine mapping segmental DNA copy number alterations. We have constructed an array of 642 ordered and fingerprint-verified bacterial artificial chromosome clones spanning the 120 megabase (Mb) 1p arm from 1p11.2 to p36.33. The 1p arm of 15 small-cell lung cancer cell lines was analysed at sub-Mb resolution using this arm-specific array. Among the genetic alterations identified, two regions of recurrent amplification emerged. They were detected in at least 45% of the samples: a 580 kb region at 1p34.2-p34.3 and a 270 kb region at 1p11.2. We further defined the potential importance of these genomic amplifications by analysing the RNA expression of the genes in these regions with Affymetrix oligonucleotide arrays and semiquantitative reverse transcriptase-polymerase chain reaction. Our data revealed overexpression of the genes HEYL, HPCAL4, BMP8, IPT, and RLF, coinciding with genomic amplification.
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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 | 0.000 |
| 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".