Sub-megabase resolution array CGH profiling of small cell lung cancer cell lines.
Bibliographic record
Abstract
3430 Background: Small cell lung cancer (SCLC) accounts for 20% of the yearly cases of lung cancer in the United States. Survival rates for this disease have improved little over the last 20 years with median survival times of only 7-13 months. This may in part be due to the high frequency of relapse with resistant micrometastatic disease after initial chemotherapy. The identification of molecular targets for prognosis and therapy of the disease may be crucial in improving survival rates of SCLC. Array comparative genomic hybridization (aCGH) is a new method for detecting genomic alterations in human cancers, which may allow the rapid discovery of novel genes involved in tumorigenesis. We have designed a CGH array which represents a >100-fold increase in resolution over conventional metaphase CGH allowing for genome-wide identification of genetic alterations at a resolution of 100kbp. Objectives: To identify novel genetic alterations in SCLC cell lines by high resolution copy number profiling of the entire human genome. Design: Utilizing a human whole-genome 32,433 overlapping BAC clone set we have created a CGH array spanning the entire genome at an average density of 10 clones per megabase. This array was then used to profile 15 SCLC cell lines for copy number changes. Sample DNA was labeled with cyanine 5 through a random priming reaction and co-hybridized with a cyanine 3 labeled reference DNA to the arrays. Post-hybridization the arrays were scanned using a CCD based imaging system from Applied Precision. Signal ratios were then determined using the Softworx array analysis program. Results: We have delineated breakpoints in SCLC lines to within one BAC clone in single experiments. This high resolution profiling has allowed us to identify both the expected alterations at loci such as hTERT, ATM and MYC as well as novel micro-amplifications and deletions as small as 200kb that have not been detected by conventional methodologies. This has allowed us to rapidly identify novel candidate genes associated with SCLC tumorigenesis. Conclusions High resolution genome wide array CGH has allowed the rapid identification of novel candidate genes associated with lung cancer tumorigenesis. This work was supported by grants from Genome Canada/B.C., NCIC Terry Fox New Frontiers, and Lung SPORE P50 CA70907.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
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".