Abstract A2-49: Integrated Omic analysis of lung cancer reveals metabolism proteome signatures with prognostic impact
Bibliographic record
Abstract
Abstract Cancer results from processes prone to selective pressure and dysregulation acting along the sequence-to-phenotype continuum DNA→RNA→Protein→Disease. 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 were collected from patient-matched normal lung, primary tumors, and patient tumor-derived xenograft (PDX) 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. Unsupervised clustering revealed signatures comprising metabolism proteins particularly highly recapitulated between matched primary and PDX tumors, and which distinguished between the major NSCLC histological subtypes adenocarcinoma (ADC) and squamous cell carcinoma (SCC). Interrogation of The Cancer Genome Atlas (TCGA) revealed sizeable cohorts of NSCLC patients with DNA alterations in genes encoding the metabolism proteome signatures, and accompanied by differences in survival. Similar to the proteome signatures from which they were extrapolated, the gene mutation signatures with prognostic impact discriminated between the lung ADC and SCC subtypes. Serine hydroxymethyltransferase 2 (SHMT2), a key enzyme in serine/glycine and folate-dependent one-carbon metabolism, is upregulated in the proteomes of NSCLC primary and PDX tumours, and is implicated as a driver of recurrent chromosome 12q14.1 amplification in NSCLC. SHMT2, along with other enzymes implicated as anti-folate targets, 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. Note: This abstract was not presented at the conference. Citation Format: Michael F. Moran, Thomas Kislinger, Lei Li, Vladimir Ignatchenko, Yuhong Wei, Christine To, Paul Taylor, Jiefei Tong, Nhu An Pham, Melania Pintilie, Lakshmi Muthuswamy, Frances A. Shepherd, Ming Sound Tsao. Integrated Omic analysis of lung cancer reveals metabolism proteome signatures with prognostic impact. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A2-49.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".