Abstract 3355: Challenges in the implementation of molecular diagnostic testing for non-small cell lung cancer
Bibliographic record
Abstract
Abstract Next generation sequencing (NGS) has been used to catalogue genetic mutations in cancer. Recent studies employing NGS have identified specific genetic mutations that reliably predict therapeutic success with targeted treatment in many forms of cancer, and particularly in non-small cell lung cancer (NSCLC). Importantly, patients with oncogenic driver mutations have better tumor control with targeted agents than with chemotherapy, while those lacking such a mutation derive more benefit from chemotherapy. To detect actionable mutations, all patients with metastatic disease must be tested. Mutation assays are generally developed using tissues derived from surgical samples. However, for many patients with metastatic NSCLC the only tissue available is from fine needle aspirates (FNAs). Given the limited number and heterogeneity of cells found in FNAs and the expanding number of clinically actionable mutations, the development and implementation of testing strategies that rapidly and accurately define driver mutations in NSCLC remains a challenge. Our project focuses on the identification of best methods (pre-analytical, analytical, and bioinformatic) to identify driver mutations in lung FNAs to standardize targeted NGS testing for NSCLC. The overarching goal of this project is to develop a strategy for Canada-wide implementation of the developed test. As a first step in this process, our team organized a stakeholder meeting to: A) Identify potential individual and/or system level challenges and barriers to implementation of standardized protocols for molecular oncology diagnostics; B) Outline guidelines and strategies to overcome identified challenges and barriers; and C) Initiate a research project to further study the barriers and facilitators of implementing Canada-wide diagnostic testing strategies for personalized cancer care. For this presentation, we will outline key challenges that impact implementation of the new test, including tumor characteristics (cellularity, heterogeneity); cost and reimbursement issues; required turn around times; bioinformatic requirements; testing strategy (technical limitations of test, panel size); technical staffing and infrastructure requirements; and barriers to implementation of the test into routine standard of care. Finally, we will present a preliminary workflow map, which builds upon the Lung Cancer Pathway Maps provided by Cancer Care Ontario (https://www.cancercareontario.ca/en/pathway-maps/lung-cancer) and addresses these barriers and explores various scenarios for implementation of new testing strategies. Citation Format: Pearl A. Campbell, Kednapa Thavorn, Bryan Lo, Ali Karimnezhad, Theodore J. Perkins, Robin Urquhart, Suzanne Kamel-Reid, Harmanjatinder Sekhon, David J. Stewart. Challenges in the implementation of molecular diagnostic testing for non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3355.
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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 | 0.193 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.011 | 0.008 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".