Use of gene set analysis (GSA) for molecular classification of responders and nonresponders to FOLFOX therapy in colorectal cancer (CRC).
Bibliographic record
Abstract
3628 Background: Folinic acid (FOL) fluorouracil (F) and oxaliplatin (OX) chemotherapy is a commonly used therapy for CRC. Lacking in current literature are clinically relevant classifiers for potential responders. GSA technique is statistical method that detects significance of sets of genes, instead of examining a gene-by-gene basis. The objective of this study was to identify differential functionally annotated gene expression profiles associated with response to FOLFOX therapy in CRC tumors using gene-by-gene and GSA approaches. Methods: Genome wide expression profile data were collected on pre-treatment tumor tissues from patients with unresectable CRC receiving FOLFOX therapy (n = 83, Affymetrix HG U133A, GSE28702, Tsuji et al. BJC 2012). Gene expression was compared between responders (n = 42) and Non-responders (n = 41). GSA was conducted on 3272 curated gene sets from the Molecular Signatures Database (Subramanian, Tamayo et al. 2005, PNAS 102, 15545-15550) annotated by biological pathway, biochemical function and clinical behavior. Significant analysis of Microarray (SAM) and GSA (Tibshirani et al.) was done to identify gene sets associated with FOLFOX response. Results: Differential expressions of 23 genes were significantly associated with response, based on a single gene approach (p-value < 0.05). 13 of these were located on Chromosome 17 (p < 0.001). Among these, the top 5 ranked genes included NPEPPS, MBTD1, CEP44, LTA4H and CPNE4 which are involved in metal ion binding and aminopeptidase activity. GSA revealed only 44 out of 3272 gene sets were significantly associated with response, with a false discovery rate less than 25%. Increased expression of B-lymphocyte differentiation and Ras-signalling-related gene sets was associated with responders while mTOR signaling and hematopoietic stem cell-related genes set were associated with non-responders. Conclusions: Our data showed that differential biological pathways could be identified to predict response to FOLFOX therapy for CRC patients. Analysis may be useful to help define clinically relevant biologic subtypes among patients with metastatic colorectal cancer.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".