Integrated Omic Analysis of Lung Cancer Reveals Metabolism Proteome Signatures with Prognostic Impact
Bibliographic record
Abstract
An Omics Array integrating DNA gene copy number, mRNA transcriptome, and quantified proteome was assembled into a genetic map representing non-small cell lung carcinoma (NSCLC). Data was from patient-matched normal lung, primary tumors, and patient tumor-derived xenograft tumors. Dysregulated proteins not previously implicated as cancer drivers were found encoded throughout the genome including but not limited to regions of recurrent DNA amplification/deletion in NSCLC. Clustering revealed signatures comprising metabolism proteins particularly highly recapitulated between matched primary and PDX tumors. Interrogation of The Cancer Genome Atlas revealed sizeable cohorts of NSCLC patients with DNA alterations in genes encoding the metabolism proteome signatures, and accompanied by differences in survival. Serine hydroxymethyltransferase 2 (SHMT2), a key enzyme in Ser/Gly/folate/1-carbon metabolism, is upregulated in NSCLC proteomes and implicated as a driver of recurrent chromosome 12q14.1 amplification. SHMT2, along with other folate metabolism enzymes is also part of a metabolism signature associated with poor outcome in lung ADC. The interrogation of cancer genomes and proteomes for alterations that are related products of selective pressures driving the cancer phenotype may be a general approach to uncover and group together cryptic, polygenic cancer drivers, which might represent new anti-cancer therapeutic targets.
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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 | 0.001 | 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".