Abstract P2-09-17: Evaluation of the oncomine comprehensive assay for the identification of actionable mutations for therapeutic stratification from the TEAM pathology cohort
Bibliographic record
Abstract
Abstract Large-scale sequencing initiatives have revealed a wealth of common and novel variants as well as copy-number aberrations, across different malignancies. This growing list of variants/aberrations can sometimes be matched to specific therapeutics. Such “actionable mutations/changes” hold promise for personalized treatment in the future, with treatments tailored to molecular abnormalities. Presently, women with hormone positive early breast cancer continue to experience improved survival on adjuvant anti-hormone therapy, but a significant number of women continue to progress. Therefore, there is a need to identify those women for whom current therapies are insufficient and to identify alternative therapeutic interventions. We explored the used of genetic profiling using a comprehensive solid tumor next generation sequencing (NGS) assay (the Oncomine Comprehensive Assay, OCA) to characterize early invasive breast cancer. The OCA is based on the Ion Torrent™ NGS platform and Ion AmpliSeq™ library preparation technology, coupled to the Oncomine™ Knowledgebase, for target selection, variant calling, and data annotations. The OCA includes 87 genes for hotspot mutation detection, 48 genes for full length sequencing and 43 genes for focal copy number assessment. The OCA provides a standardized informatics workflow and quality control (QC) parameters to process samples in a translational clinical research setting. To explore the application of the OCA to early invasive breast cancers, we performed a retrospective pilot study in a subset of cases from the TEAM trial. From the TEAM pathology samples, 420 were chosen in a case-control fashion, 413 samples were analyzed, 388 samples passed standard QC metrics, and 254 samples (65%) were found to contain 368 variants with Oncomine Knowledgebase annotations. Briefly, variants of PIK3CA were most frequent at 42.7% (157/368), followed by TP53 at 27.2% (100/368), PTEN at 5.7% (21/368), BRCA2 at 3.8% (14/368), SF3B1 (12/368), AKT1 (11/368) and PTCH1 (11/368) at 3.3%, 3.0%, 3.0%; respectively. Other variants were detected in ATM, ERBB2, RB1, FGFR2, NF1, CDKN2A, PIK3R1 and others. Amongst the 43 genes assessed for copy-number, 23 showed copy-number changes across 132 samples totalling 167 CNVs. 256 samples showed no copy-number alterations in any of the genes on the panel. ERBB2 was most frequently altered at 28.1% (47/167), followed by FGFR1 at 23.4% (39/167), CCND1 at 15.0% (25/167) and MDM2 at 10.2% (17/167). Copy-number losses were identified in TP53, RB1, PTEN, BRCA2 at 0.6% each; as well as CDKN2A at 1.8% (3/167). Analytical validation of a subset of gene variants and copy-number changes will be presented in addition to the evidence of potential future application of the Oncomine Comprehensive Assay to precision oncology goals. Citation Format: Bayani J, Crozier C, Quintayo MA, Amemiya Y, Zhang X, Larivière M, Sadis S, Smith JM, Hasenburg A, Kieback D, Markopoulos C, Dirix L, Yaffe M, Seth A, Feilotter H, Rea D, Bartlett JMS. Evaluation of the oncomine comprehensive assay for the identification of actionable mutations for therapeutic stratification from the TEAM pathology cohort [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P2-09-17.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".