Abstract 3994: Identifying molecular networks linked to colorectal liver metastasis progression during liver regeneration by RNA-seq
Bibliographic record
Abstract
Abstract The only potential cure for colorectal liver metastasis (CRCLM) is hepatic resection; portal vein embolization (PVE) is used as an adjunct to stimulate liver regeneration and increase the number of resectable patients. PVE has been shown to enhance CRCLM progression in most patients. The objective of this study is to determine the molecular networks involved in CRCLM progression during liver regeneration, by transcriptome analysis (RNA-Seq). CRCLM RNA was procured prospectively from patients prior to and after PVE. 14 patients with PVE underwent tumor volumetric analysis (blinded from the transcriptome analysis) to determine stable vs. progressive disease. A subgroup of 9 patients had tumor progression (median 45.3% (95%CI 10.6-192.5%) increase in tumor volume) while the remaining patients exhibited stable disease and continuous chemotherapy response (median 26.0%(95%CI -53.1-1.2%) decrease in tumor volume) (p=0.0121), which was consistent with our published series of 123 patients. RNA extracted from tumor tissue (pre and post PVE) in a subset (n=6) of patients (median RNA Integrity number (RIN) scores 7.1 (6.2 - 9.0)) was used to prepare Illumina rRNA-depleted TruSeq stranded cDNA libraries for HiSeq 100bp paired-end sequencing. An average of 150 million reads and 91.8% genomic alignment was achieved per sample. The analysis revealed that several genes known to act as tumor suppressors in CRC (e.g. NR1H4 and HRG) are consistently down regulated post-PVE in the progressive group. Several canonical pathways (including acute phase response, LXR/RXR signaling) showed statistical enrichment for transcripts that were differentially expressed in patients with disease progression. This work aims to establish a comprehensive view of the transcriptional changes associated with CRCLM progression during liver regeneration, to identify the molecular mechanisms that drive metastatic progression in this unique context. Highlighting such mechanisms is a critical first step towards developing targeted therapeutic strategies that may mitigate the unwanted effects of liver regeneration on tumor growth. Citation Format: Eve Simoneau, Jarred Chicoine, Ayat Salman, Robert Sladek, Anthoula Lazaris, Ramila Amre, Peter Metrakos. Identifying molecular networks linked to colorectal liver metastasis progression during liver regeneration by RNA-seq. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3994. doi:10.1158/1538-7445.AM2014-3994
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.004 | 0.002 |
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".