Abstract 5069: Genomic profiles of primary non-small cell lung cancer (NSCLC) xenograft tumors identify distinct gene signatures associated with histological subtypes
Bibliographic record
Abstract
Abstract Xenografts established directly from patient tumors mirror closely the histology of the primary tumors. Therefore, primary tumor xenografts (PTXG) may serve as important preclinical models to evaluate novel anti-cancer drugs. We previously reported that the ability of resected tumors to engraft in NOD-scid mice is a strong predictor of relapse after surgery and poorer prognosis in NSCLC patients, and thus may represent biologically more aggressive cancers (Clin Cancer Res 2011;17:134-41). Genomic characterization of PTXG would help identify genetic aberrations that drive malignant oncogenic pathways in NSCLC. We characterized the somatic copy number alterations (CNA) of 36 PTGX, consisting of 15 adenocarcinoma (ADC), 18 squamous cell carcinoma (SCC), 2 large cell neuroendocrine carcinoma (LCNEC) and 1 large cell carcinoma (LC), along with 34 patient normal samples as controls using Illumina Omni-1 Quad SNP arrays. The gene expression profiles of the 36 PTGX were analyzed using Illumina Omni-1 Quad HT-12 v4 arrays. Histology-specific recurrent regions of CNA observed in PTGX are concordant with the published and publicly available primary NSCLC CNAs. We identified 1053 genes with somatic copy number gains and 932 genes with somatic copy number losses that distinguish between SCC and ADC. From integrative analysis of mRNA expression and somatic CNAs, we identified 325 genes specific to ADC and 2232 specific to SCC that are well correlated. Gene candidates that are deregulated in ADC include WRN, STK35, SIX1; and genes that are over-expressed in SCC include SOX2, RNF13, WNK1, PIK3CA, TFRC, TP63, PAK2 suggesting there is differential deregulation of signaling pathways between these two subtypes of lung cancer. We have identified candidate gene signatures that distinguish between ADC and SCC from PTXG, suggesting these xenograft models can provide a valuable resource to study cancer biology and preclinical drug target evaluation in vivo. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5069. doi:1538-7445.AM2012-5069
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.004 | 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".