Abstract 3575: The OncoNetwork Consortium: A global collaborative research study on the development and verification of an Ion AmpliSeq RNA gene lung fusion panel
Bibliographic record
Abstract
Abstract Chromosomal translocations and corresponding gene fusions play an important role in the initiation of tumorigenesis and these processes have been strongly associated with distinct tumor subtypes. The recent association of ALK, ROS and RET fusion transcripts as lung tumor therapy predictive biomarkers has increased the need of a technology that could detect these biomarkers starting from limited amount of material. Life Technologies and OncoNetwork consortium collaborated for the development of a lung fusion panel based on Ion AmpliSeq™ RNA chemistry. The OncoNetwork consortium is comprised of twelve -translational cancer research institutes with many years of experience in adopting the latest molecular techniques for lung therapy research. Material and Methods: Consortia's requirements for the panel development : 1) detect all variants of ALK, ROS1, or RET fusion transcripts described in COSMIC in a single reaction using 10 ng of total RNA 2) Include 5′ and 3′ ALK, ROS1, RET gene expression assays as an indicator of a translocation at this gene. 3) Include endogenous RNA assay controls to determine if the quality of the results could be affected by RNA quality. 4) Provide similar results on archived FFPE samples tested by FISH. Human lung adenocarcinoma cell lines H2228 (EML4-ALK positive), HCC78 (SLC4A2-ROS1 positive ) ,LC-2/ad (CCDC6-RET positive) and Ambion® FirstChoice® Human Brain Reference (HBR) RNA was used as positive and negative control for the study. Formalin fixed paraffin embedded (FFPE) tissue was isolated using different extraction methods. After amplification using Ion AmpliSeq™ RNA chemistry samples were sequenced on the Ion Torrent PGM™ sequencer using the Ion PGM™ 200 Sequencing Kit. The presence of the fusions was confirmed by custom TaqMan® gene expression assays when possible. Preliminary results of the panel on ALK or ROS1 or RET positive cell lines and FFPE archived cancer research samples gave good concordance with FISH results. Expected negative samples were confirmed negative. We found a KIF5B-RET fusion positive sample in a sample not previously tested for RET fusions. Two of the expected positive samples by FISH were found negative due to limited amount to tumor cells present in the sample. Cell line RNA dilutions were performed to determine the panel's limit of detection. We demonstrate a limit of detection of 1 % tumor RNA in the presence of 99 % normal RNA using the panel with 10 ng of RNA extracted by cell lines. Gene expression controls work well as an indicator of the RNA quality and of the translocation presence. The Ion AmpliSeq™ RNA lung cancer fusion panel workflow is easier and faster to perform in comparison to the FISH method. The results obtained to date are highly encouraging for panel to be used in the clinical research setting. More data needs to be analyzed before a final conclusion is made. Citation Format: Susan M. Magdaleno, Angie Cheng, Rosella Petraroli, Orla Sheils, Bastiaan Tops, Delphine Le Corre, Henriette Kurth, Helene Blons, Eliana Amato, Andrea Mafficini, Anna Maria Rachiglio, Anne Reimann, Christoph Noppen, Chrysanthi Ainali, Jin Katayama, Renato Franco, Harriet Feilotter, Jeoffrey Schageman, Ian Cree, Andrew Felton, Jose Luis Costa, Alain Rico, Aldo Scarpa, Jose Carlos Machado, Kazuto Nishio, Nicola Normanno, Marjolijn Ligtenberg, Cecily P. Vaughn, Ludovic Lacroix, Pierre Laurent-Puig. The OncoNetwork Consortium: A global collaborative research study on the development and verification of an Ion AmpliSeq RNA gene lung fusion panel. [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 3575. doi:10.1158/1538-7445.AM2014-3575
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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 | 0.040 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".