Practical application of whole genome and transcriptome tumour analysis to guide chemotherapy decision-making for patients with advanced cancers.
Bibliographic record
Abstract
e22020 Background: We propose that applying personal genomic information prospectively, in a clinically realistic timeframe can aid chemotherapy decision-making and result in more effective cancer treatment. We are investigating this approach in a variety of cancers to examine timeliness, deliverability, and rate of actionable targets identified. Methods: Eligible subjects with incurable cancer and limited chemo options have a tumour biopsy and “normal” blood taken for analysis. Archival specimens are concurrently analyzed to look for changes with time and treatment. Samples are subject to both an Ampliseq amplicon panel and in-depth whole genome DNA and RNA sequencing (WGS). Bioinformatics approaches identity genes with somatic and copy number variations, and expression changes. Variants are integrated into a pathway analysis to identify tumour specific processes that may drive the tumour, these are then matched to drug databases, with manual literature reviews, to indentify drugs that may be useful or even contra-indicated. Results: Between July 2012 -Jan 2013, 9 subjects (of 30 planned) are enrolled: 2 cases each of: colorectal and breast and 1 each of: squamous skin, squamous ethmoid sinus, nasopharyngeal, lung, and CLL-peripheral mantle cell cancer. 5 have completed analyses. Cancer panel results correlated well with WGS; although the panel is more rapid, it provides less comprehensive information and has not been as informative for identifying candidate druggable drivers. Extensive pathway mapping uncovered potential drug targets in each case that would not have necessarily been considered without this analyses. To date, 4 subjects have started chemo based on the analyses and 1 patient has had his diagnosis radically changed. There are significant genomic differences between archival and fresh tumour samples. Conclusions: This approach is feasible and yields actionable targets that can inform real-time chemotherapy decision-making. Archival samples do not appear to adequately represent post-treatment cancers. The impact of WGS vs. panel sequencing will require more subjects but it appears a panel may be insufficient for detailed treatment guidance.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".