Abstract 4283: Whole genome sequencing is superior to cancer panels to aid in decision-making in patients with advanced malignancies
Bibliographic record
Abstract
Abstract Background: It is increasingly common to use targeted cancer panels to assess for informative or actionable targets to guide cancer treatment. We compared the utility of information obtained from such a panel compared to whole genome and transcriptome sequencing. Methods: Eligible subjects with incurable cancers for whom there were limited or no standard chemotherapy options, have several samples analyzed: a fresh tumour biopsy; a blood sample for normal comparison; and archival tumours 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 (diagnostic) or actionable/druggable targets. Aberrant pathways were matched to drug databases and manual literature reviews undertaken to identify drugs that may be useful or potentially contraindicated; a report is generated and discussed in a multidisciplinary team. Results: Between July 2012 - November 2013, 51 subjects have consented and 45 have been sequenced: 12 breast, 7 lung; 4 colorectal, 3 squamous, 3 adrenal; 2 pancreas; 2 sarcomas, and 1 of each of nasopharynx, primary unknown, CLL-peripheral mantle cell, parotid, anal, appendix, peripheral T-cell, prostate, ovary, endometrial, glioma, and mesothelioma. The median number of lines of chemo prior to sequencing was 3. Of the first 40 cases, the panel did not yield informative or actionable results in 60% of cases: 36% of the cases the panel did not detect any somatic variants and in a further 24% TP53 mutations were the only variants identified. In the other 40% of cases, the panel might have identified an informative abnormality but when put into context of the pathways that can be drawn with the whole genome and transcriptome data the detected panel abnormalities were not comprehensive enough to guide treatment decisions. Examples include: in two colon cancer patients the panel detected a KRAS mutation in one and a RET mutation in the other, however both were actually inactivating mutations and therefore unlikely to be good targets. In contrast, the full genomic data was informative in 70% of cases and treatments were delivered based on the results in 60%, in this heavily pretreated population. Conclusions: The future of cancer medicine lies in genomic profiling to assist therapeutic decision-making. The cancer panel has advantages in terms of speed and cost; however it has not been as informative for identifying candidate druggable driver events and it has missed critical genomic abnormalities that have changed diagnoses. Given the complexity of the cancer genome it is our observation that the panels do not provide the depth and context required to make treatment decisions for the majority of patients with cancer. Citation Format: Janessa J. Laskin, Yaoqing Shen, Howard Lim, Karen A. Gelmon, Daniel Renouf, Stephen Yip, David Huntsman, Anna Tinker, Cheryl Ho, Stephen Chia, Yvonne Li, Katayoon Kasaian, Peter Eirew, Sreeja Leelakumari, Richard Moore, Samuel Aparicio, Yusanne Ma, Steven Jones, Marco Marra. Whole genome sequencing is superior to cancer panels to aid in decision-making in patients with advanced malignancies. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4283. doi:10.1158/1538-7445.AM2014-4283
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".