The use of whole-genome sequencing in therapeutic for decision making in patients with advanced malignancies.
Bibliographic record
Abstract
11026 Background: We used information obtained from whole genome and transcriptome sequencing to aid in therapeutic decision-making in patients (pts) with advanced cancers. Methods: Eligible pts with incurable cancers with limited or no standard options, had several samples analyzed: a fresh tumor biopsy; a blood sample for normal comparison; and archival tumor tissue when available. Samples underwent both the Ion Torrent AmpliSeq cancer panel analysis and comprehensive DNA (80X) and RNA sequencing followed by in-depth bioinformatic analysis to identify somatic mutations, copy number alterations, structural rearrangements, and corresponding gene expression changes that may be cancer “drivers” or provide informative/diagnosticor actionable targets. Aberrant pathways were matched to drug databases and manual literature reviews were performed to identify drugs that may be useful or potentially contraindicated. A report was generated and discussed in a multidisciplinary team. Results: Between July 2012 - January 2014, 65 pts had consented (including 4 pediatrics cases) and 56 have been sequenced: 18 breast, 8 lung; 4 colorectal, 3 squamous, 3 adrenal; 2 pancreas; 2 sarcomas, 2 neurofibroma, 2 mesothelioma, and 1 of each of nasopharynx, primary unknown, CLL-peripheral mantle cell, parotid, anal, appendix, peripheral T-cell, prostate, ovary, endometrial, glioma, and leiomyoma. The median number of lines of chemo prior to sequencing was 3. The AmpliSeq panel only yielded actionable targets in 40% of cases. The full genomic data was more comprehensive about driver pathways and was informative in 70% of cases. Clinical data is available on 30 pts. In 21 pts data was actionable. In 3 pts, the diagnosis was changed and 9 pts died before the results could be used. Treatments were delivered based on the results in 8 pts, 6 (75%) of these pts derived clinical benefit from treatment based on genomic therapy. Conclusions: Whole genome sequencing based therapeutic decision-making in the management of advanced cancer is feasible. The information is more comprehensive than panels and yields clinically actionable data not identified by panel sequencing. Further studies are needed to determine the utility of this technology.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".