A single center survival analysis of patients with colorectal cancer who underwent next generation sequencing.
Bibliographic record
Abstract
31 Background: Colorectal cancer (CRC) remains a lethal diagnosis with an overall 5-year survival rate of 5-10% for patients with unresectable metastatic disease. The advent of next generation sequencing allows tailored systemic therapy to specific mutations with the goal of improving patient survival. McGill University Health Center instituted next generation sequencing in 2019. Our study aims to evaluate survival outcomes in patients who underwent NGS testing. Methods: A retrospective collection of data on all patients with CRC who were presented at both lower gastroenterology and hepatobiliary tumor boards from January 2019 through July 2022 ( n= 498). Survival and was compared between patients who were found to have genetic alterations identified using the Illumina Miseo platform. Statistical Analysis was performed with GraphPad Prism. Results: A total of 321 (64%) patients had NGS performed on either their primary tumor or a metastasis. A total of 229 (71%) CRC patients had genetic alterations identified on NGS. The most commonly mutated genes were KRAS (41%), APC (17%), PIK3CA (16%) and BRAF (8%). There was no significant difference in median overall survival between patients who had an identified genetic alteration and patients who did not (p = 0.8). Patients with metastatic disease and synchronous presentation had higher rates of genetic alterations (69% and 70%, respectively). Conclusions: This study provides real world data for a single institution initiating Next generation sequencing in colorectal cancer patients. Next Generation testing is an advantageous tool which can help stratify patients into tailored treatment regiments which will lead to improved patient outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".