Abstract LB-173: Genome analysis informs chemotherapy decision-making in patients with advanced malignancies.
Bibliographic record
Abstract
Abstract Background: Tumor heterogeneity poses a significant challenge to the success of treatment; tumors with similar histological features may have substantially different underlying biological drivers. Applying personal genomic information prospectively to assist in chemotherapy decision-making could result in more effective and efficient cancer treatment. Methods: Eligible subjects with incurable cancers for whom there are limited or no standard chemotherapy options, have a tumor biopsy and when possible a tissue matched normal sample plus “normal” blood samples taken specifically for genomic analysis. Archival specimens are concurrently analyzed to look for changes over time and with chemo and/or radiation. Samples are subject to both an Ampliseq amplicon cancer panel analysis and whole genome DNA and RNA sequencing. State-of-the-art bioinformatics approaches are used to identity genes with somatic variants, copy number variants, and expression changes. All variants are integrated into a pathway analysis to identify tumor specific biological processes that may be driving the tumor. These pathways are matched to the known drug databases and to manual literature reviews to identify drugs that may be useful or drugs that may be counter-indicated and a report is generated and discussed. Results: Between July 2012 and January 2013, 10 subjects (of 30 planned) have been enrolled; 3 cases of colorectal and 2 of breast, 1 each of squamous skin, squamous ethmoid sinus cancer, nasopharynx, lung, and one CLL-peripheral mantle cell cancer. The Ampliseq panel results have generally correlated well with whole genome and RNA sequencing results, although the panel, providing less comprehensive information albeit more rapidly, has not been as informative a modality for identifying candidate druggable driver events. And the case of ethmoid cancer was discovered to be a rare pediatric tumor, this was not identified by the panel. Each case had extensive pathway mapping and uncovered potential drug targets that would not have necessarily been considered without this analyses. To date, 3 subjects have initiated treatment based on the reports generated; the analyses are in process for the remaining cases. We also note that we observe significant genomic differences between the archival and fresh tumor materials. Conclusions: Initial results suggest that the information garnered from detailed genomic analysis can inform chemotherapy decision-making. The panel is adept at profiling the common abnormalities that are the target of many of the current generation of molecular targeted drugs; however the whole genome approach provides a comprehensive view of the genomic landscape providing more information on particular aberrant pathways affected and ideas for drug repositioning. For now we have elected to employ whole genome and transcriptome analysis in addition to an “oncogene panel” to both compare the relative utility of these two approaches and provide as comprehensive a view of candidate druggable driver events across patients to inform on the next generation of rapid panel designs. Citation Format: Janessa J. Laskin, Karen Gelmon, Howard Lim, Daniel Renouf, Stephen Yip, David Huntsman, Anna Tinker, Cheryl Ho, Erin D. Pleasance, Yvonne Li, Yaoqing Shen, Katayoon Kasaian, Richard Corbett, Karen Mungall, Yongjun Zhao, Andy Mungall, Jacquie Schein, Robyn Roscoe, Steven Jones, Marco Marra. Genome analysis informs chemotherapy decision-making in patients with advanced malignancies. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr LB-173. doi:10.1158/1538-7445.AM2013-LB-173
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".