A case of the utilization of genomic information in the management of metastatic colorectal cancer.
Bibliographic record
Abstract
444 Background: Anectodal success using genomic information to personalize and guide cancer therapy in rare tumours has been reported. This is a case report of the use of genomic information in the management of metastatic colorectal cancer. Methods: A 31 year old female presented with metastatic disease from the colon to the liver and retroperitoneal lymph nodes. She was treated with aspirin and 4 cycles of 5FU/Irinotecan/Bevacizumab before blood and liver biopsies were taken. Sections from the primary tissue embedded in paraffin were compared to metastatic disease in the liver and blood. Two platforms were used: a targeted deep sequencing of 46 genes via a cancer specific amplicon assay and whole genome sequencing and RNA sequencing followed by bioinformatics approaches were employed to identify genes with somatic variants, copy number variations, losses of heterozygosity, structural variation, and expression changes. Results: Genome sequencing followed by bioinformatics analysis detected ~100 somatic mutations, ~100 amplified genes with increased expression, and ~90 down-regulated genes with copy number loss in the metastasis. Other interesting observations included increased allelic frequencies in BRAF V600E, and SMAD4 R361C as well as high gene copy amplifications in VEGFA and MAGI from the primary to the metastasis, and lack of somatic mutation in KRAS. Conclusions: The upregulation of VEGF pathways from the primary compared to the metastasis suggests a significant shift in tumour biology likely as a result of the bevacizumab and COX-2 inhibition from aspirin; this may have implications when anti-VEGF therapy is withdrawn. The BRAF mutation and wild type KRAS status may suggest that combination of a BRAF inhibitor with EGFR inhibition is warranted. This information demonstrated the benefit of therapy on a genomic level and aided in determining possible future targets and modifications to conventional therapy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".