Whole-genome DNA and RNA sequencing in patients with metastatic colorectal cancer (mCRC).
Bibliographic record
Abstract
e22078 Background: mCRC carries a high mortality rate, and invariably becomes resistant to treatment. Whole genome sequencing (WGS) of DNA and RNA, and bioinformatic analysis can be performed in a clinically relevant timeframe. We describe our experience with WGS in 3 pts with mCRC. Methods: Pts with refractory mCRC were recruited. Fresh tumor biopsy, blood samples, and archival tumors were analyzed. Samples underwent Ion Torrent AmpliSeq cancer panel analysis and comprehensive DNA (80X) and RNA sequencing followed by in-depth bioinformatic analysis. Signalling pathway abnormalities including somatic mutations, copy number alterations, structural rearrangements, and corresponding gene expression changes were identified. Aberrant pathways were matched to drug databases and literatures searches to identify potential therapeutics. Results: We recruited 3 pts with mCRC, all of whom had received at least two lines of systemic therapy. Pt #1 was a 30 yo woman with KRAS-wild-type mCRC. Tissue was analyzed from the liver and primary colon lesions. The tumor was found to have a BRAF V600E mutation. In the metastatic lesion, a striking upregulation of VEGF-A was seen compared with archival tissue, possibly representing the tumor’s response to bevacizumab therapy. There was also upregulation of MAGI1 which may reflect COX-2 inhibition from aspirin treatment. Pt #2 was a 64 yo woman with KRAS-mutant mCRC. Tissue was analyzed from an abdominal wall mass. Aberrations were concentrated in FGFR, cell cycle, Notch, and Wnt signaling pathways. While originally characterized as KRAS-mutant, WGS revealed that this activating KRAS mutation was not actually expressed in tumor cells. Pt #3 was a 63 yo male with KRAS-mutant mCRC. Tissue was analyzed from a liver biopsy. Aberrations again were noted in pathways involving FGFR, and WNT. Mutations were noted in a number of tumor suppressor genes including TP53, APC, and EPHA3. In each case, plausible drug options were identified to target these pathways. Conclusions: WGS is a useful tool to interrogate tumor biology, and can reveal abnormalities in signaling transduction in patients with mCRC. This information may guide therapeutic strategies beyond what is yielded from standard mutation panels.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".