Abstract 7408: Robust profiling of cancer-related gene fusions: Analytical validation of PANCARNA for multiple cancer types
Bibliographic record
Abstract
Abstract The impact of examining gene fusion presence in cancer patients is often overlooked, and as such, no well-established workflow currently exists for robust profiling of tumor samples in this area. We established a targeted RNA sequencing panel (PANCARNA) for investigating gene fusions across multiple cancer types and performed analytical validation to confirm performance. Furthermore, we report on the applicability of using targeted RNA sequencing for the purposes of gene expression profiling. This panel covers 117 genes associated with solid tumors. Thirty-nine FFPE samples with gene fusion status validated using an orthogonal method were used to evaluate analytical performance. Of the 39 standard samples, 33 samples were positive for gene translocation events and 6 were negative controls. Samples were collected from across 10 cancer types. To examine the robustness of the panel, 12 samples with known fusions were selected to undergo testing. Each sample was tested in triplicate across two experimental batches. Seraseq Fusion RNA Mix V4 (SeraCare, Cat. 0710-0497) standard samples were used to test the limit of detection for PANCARNA. Additionally, we explored a cohort of 22 samples with over expressed genes and 4 samples without overexpressed genes to test the expression profiling capabilities of PANCARNA and compared the results to immunohistochemistry stains. Of the 39 samples tested using the orthogonal method and targeted RNA-Seq, 38 were found to have concordant fusion calls in both methods (Positive percent agreement = 97.0%, 32/33; Negative percent agreement = 100%, 6/6; Overall percent agreement = 97.4%, 38/39). In reproducibility studies, the assay produced consistent intrarun and interrun results in all samples (Repeatability = 100%, 12/12; Reproducibility = 100%, 12/12). The limit of detection for multiple fusions of interest, such as EML4-ALK, CD74-ROS1, TPM3-NTRK1, and SLC34A2-BRAF, is found to be 0.5-1 copies per ng of RNA. ETV6-NTRK1 can be reliably detected at 2-4 copies per ng of RNA. MET ex14 skipping events can also be detected in a few as 1-2 copies per ng of RNA. To validate the capability of PANCARNA to quantify gene expression, we tested 26 samples with known gene expression levels for three genes of interest (ERBB2, EGFR, MET). ERBB2, EGFR and MET amplification detection through PANCARNA was found to have a high concordance to IHC results (Sensitivity = 96.2%, 100%, 94.7%, respectively). In conclusion, PANCARNA demonstrated highly accurate and sensitive detection of clinically relevant gene fusions. This assay produces reliable results when detecting fusions and offers high concordance to results obtained through IHC/FISH, indicating that targeted RNA seq analysis can assist with profiling gene fusion events in a clinical setting. Citation Format: Haimeng Tang, Hua Bao, Rui Liu, Shuyu Wu, Sisi Liu, Xue Wu, Yang Shao. Robust profiling of cancer-related gene fusions: Analytical validation of PANCARNA for multiple cancer types [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7408.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".