Patients’ and Oncologists’ Knowledge and Expectations Regarding Tumor Multigene Next-Generation Sequencing: A Narrative Review
Bibliographic record
Abstract
BACKGROUND: Tumor multigene next-generation sequencing (NGS) is increasingly being offered to cancer patients to guide clinical management and determine eligibility for clinical trials. We undertook a review of studies examining the knowledge and attitudes of patients and oncologists regarding the primary results and potential secondary findings of such testing. MATERIALS AND METHODS: A search was conducted through the MEDLINE database using the following keywords: "neoplasms" and "molecular sequencing / genome sequencing / tumor profiling / NGS / whole exome sequencing" and "patient / oncologist" and "knowledge / attitudes / satisfaction / experience / evaluation / perspective / practice / preference." Articles meeting the inclusion criteria and additional relevant articles from their references were selected. RESULTS: From 1,142 publications identified by the search and 9 from references, 21 publications were included in the final review. Patients generally had positive attitudes toward tumor NGS despite relatively little knowledge of test-related genetics concepts, but their expectations often exceeded the reality of low clinical utility. Patients with higher education and greater genetics knowledge had more realistic expectations and a more altruistic view of the role of NGS. Attitudes toward disclosure of secondary findings were highly variable. Oncologists had poor to moderate genomic literacy; they communicated challenges with tempering patient expectations and deciding what information to disclose. CONCLUSION: Patients considering undergoing tumor NGS should be provided with easily understandable resources explaining the procedure, goals, and probable outcomes, whenever possible based on evidence-based guidelines. Continuing medical education programs on this topic for oncology health care professionals should strive to improve their genomic literacy and instruct them on how to optimally present this information to their patients. IMPLICATIONS FOR PRACTICE: Oncologists are increasingly offering tumor multigene testing to patients with advanced cancers to guide more "personalized" treatment and/or determine eligibility for clinical trials. However, patients often have inadequate understanding and unrealistic expectations. Oncologists must ensure that they themselves have sufficient knowledge of the benefits and limitations of testing and must provide their patients with appropriate educational resources. Prior to testing, patients should be told the likelihood of finding a mutation in their specific tumor type for which a targeted treatment or clinical trial is available. Patients also need clear information about the possibility and implications of secondary findings.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".