Abstract 4912: Systematic, comparative network analysis on non-small cell lung cancer
Bibliographic record
Abstract
Abstract BACKGROUND: Most cancers lack any effective early disease markers, prognostic and predictive signatures. We fail treating cancer due to multiple ways cancer initiates and develops treatment resistance. While drug modes of action are complex and poorly understood, using Comparative Toxicogenomics Database is an effective way to identify drug combinations. Integrating signatures with deregulated network information may lead to identifying novel treatment option for individual patients. APPROACH: A systematic graph analysis was used to extract network structure differences between normal and tumor patient samples in non-small cell lung cancer. Three gene expression datasets with 27 squamous cell carcinoma, 129 adenocarcinoma, 20 large cell carcinoma and 141 normal samples, and 18 prognostic non-small cell lung cancer gene signatures were used to construct normal and tumor co-expression graphs. RESULTS: We enumerated all 5-node graphlets in normal and tumor graphs, and separated them into 3 categories: unique for normal graph, unique for tumor graph, or present in both. We further focused on subgraphs with the same membership across all 3 datasets and unique to tumor graph. Using gene enrichment analysis with a hypergeometric test we identified 9 subgraphs significantly enriched in the term “regulation of lymphocyte activation” (p<0.05), and genes related to chemokine receptors (CCR2, CCR7), interleukin (IL16), interleukin receptor (IL7R), interferon regulatory factor (IRF4), and T cells or B cells (PTPRCAP, SH2D1A, LCK, BTK, MS4A1). Importantly, this analysis identified protein interaction deregulated in tumors. Using the Comparative Toxicogenomics Database, we identified putative compounds that may “repair” the wiring of these subnetworks in tumor samples. 7 out of 7 identified compounds for the significantly up-regulated genes (p<0.05), and 20 out of 25 significantly down-regulated genes (p<0.05) are associated with non-small cell lung cancer. The other 5 identified compounds are known for other types of cancer or neoplasms, including lung neoplasms. Importantly, 13/38 edges have known/predicted protein interaction evidence, with a high prediction scores >0.9 (11 interactions) and 0.8 (2 interactions). CONCLUSIONS: Systematic integration and network analysis of non-small cell lung signatures identifies potential treatment options and insights to the difference in the underlying wiring related to immune system, an emerging hallmark of cancer. 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 4912. doi:1538-7445.AM2012-4912
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".