A Validated Prognostic Multigene Expression Assay for Overall Survival in Resected Colorectal Cancer Liver Metastases
Bibliographic record
Abstract
PURPOSE: Risk stratification after surgery for colorectal cancer liver metastases (CRLM) is achieved using clinicopathologic variables, however, is of limited accuracy. We sought to derive and externally validate a multigene expression assay prognostic of overall survival (OS) that is superior to clinicopathologic variables in patients with surgically resected CRLM. EXPERIMENTAL DESIGN: We measured mRNA expression in prospectively collected frozen tumor from 96 patients with surgically resected CRLM at Memorial Sloan Kettering Cancer Center (MSKCC, New York, NY). We retrospectively generated a 20-gene molecular risk score (MRS) and compared its prognostic utility for OS and recurrence-free survival (RFS) with three common clinical risk scores (CRS). We then tested the prognostic ability of the MRS in an external validation cohort (European) of 119 patients with surgically resected CRLM at the University Medical Center Utrecht (Utrecht, the Netherlands) and Paul Brousse Hospital (Villejuif, France). RESULTS: For OS in the MSKCC cohort, MRS was the strongest independent prognosticator (HR, 3.7-4.9; P < 0.001) followed by adjuvant chemotherapy (HR, 0.3; P ≤ 0.001). For OS in the European cohort, MRS was the only independent prognosticator (HR, 3.5; P = 0.007). For RFS, MRS was also independently prognostic in the MSKCC cohort (HR, 2.4-2.6; P ≤ 0.001) and the European cohort (HR, 1.6-2.5; P ≤ 0.05). CONCLUSIONS: Compared with CRSs, the MRS is more accurate, broadly applicable, and an independent prognostic biomarker of OS in resected CRLM. This MRS is the first externally validated prognostic multigene expression assay after metastasectomy for CRLM and warrants prospective validation. Clin Cancer Res; 22(10); 2575-82. ©2016 AACR.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".