Abstract 1308: Policy guiding secondary findings from genome sequencing pertinent to cancer: Results from a systematic review
Bibliographic record
Abstract
Abstract Background: While genomic sequencing has personalized patient care across disciplines like oncology, secondary findings (SFs) unrelated to the primary indication are a common complicating factor. SFs relevant for oncology are categorized as cancer-related potentially leading to a cancer diagnosis (CA-SFs) or non-cancer findings revealing important medical conditions, predispositions, or carrier status in cancer patients (NC-SFs). These SFs can inform disease prevention, early detection, or management decisions, but may cause distress, overdiagnosis, and overuse of resources if actionability is limited. There is a lack of consensus for identifying, analyzing, and interpreting the clinical relevance of SFs. This significantly impacts cancer care, where paired tumor-normal sequencing is standard practice. Widespread practice variation can lead to detrimental impacts on patient outcomes and overall health. We synthesized policy guiding the clinical investigation of SFs for cancer to help providers navigate decisions regarding SFs. Methods: We carried out a systematic review of international guidance directing the identification, analysis, and management of SFs from genomic sequencing, and analyzed the subset of policies specifically relating to oncology. We searched the grey literature (IFHGS members) and academic databases including MEDLINE, Embase and Cochrane. Two reviewers independently assessed policy documents for screening, data extraction and quality assessment (using AGREE-II). Results: We identified 7 policies guiding the investigation of SFs relating to cancer across the general population (n=4), adults (n=1), pediatrics (n=1) and research (n=2) contexts. Most policies included guidelines for SF variant selection (identification; n=5) and management (n=4), with a minority detailing analysis processes (n=2). Recommended CA-SFs included medically actionable cancer predisposition variants, while suggested NC-SFs were variants important for drug therapies (identification). Laboratories were advised to ensure adequate coverage and read depth during SF analysis (analysis), while policies recommended that a medical genetics healthcare provider return results, specifically discussing their clinical utility (management). Conclusions: To our knowledge, our review is the most comprehensive synthesis of policy guiding SFs for cancer. Policies outline the types of SFs to investigate from genome sequencing in cancer and non-cancer patients, and related strategies for disclosure. However, best practices for SF analyses and patient follow-up including SF surveillance, treatment, etc. remain poorly described. This synthesis will help cancer care providers navigate critical decision points based on current evidence and direct policymakers on gaps to fill in the future. Citation Format: Safa Majeed, Christine Johnston, Saumeh Saeedi, Chloe Mighton, Vanessa Rokoszak, Ilham Abbasi, Sonya Grewal, Vernie Aguda, David Malkin, Yvonne Bombard. Policy guiding secondary findings from genome sequencing pertinent to cancer: Results from a systematic review [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 1308.
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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 | 0.113 | 0.399 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.023 | 0.024 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".