Abstract 1832: Large variations in clinical and ethical aspects of genomic sequencing initiatives: A Global Alliance for Genomics and Health (GA4GH) survey
Bibliographic record
Abstract
Abstract Although next generation sequencing (NGS) has expanded our understanding of disease prognostication and cancer treatment, there is heterogeneity regarding its implementation. GA4GH is a not-for-profit organization that promotes and harmonizes responsible and effective data sharing, as unconnected data silos unacceptably stall the advancement of precision medicine. The GA4GH Cancer Task Team conducted a survey of international cancer sequencing activities to evaluate variability in these initiatives and report our findings on clinical/ethical aspects. A total of 108 sequencing initiatives were approached via a web-based survey, of which 59 responded (55%) (Characteristics: Table 1). Most initiatives (61%) were North American or European based. Genomic-based drug matching occurred in 39 initiatives (66%): unplanned opportunistic matching to existent trials e.g. phase I (n = 29), specifically designed genomics driven trials (n = 10). In matching initiatives, outcome data was collected via RECIST in 24 (62%), time on treatment in 23 (59%), and clinical assessment in 10 (26%). Toxicity data was collected in all clinical trials but in few genomic sequencing programs. Specific or implied informed consent was identified in 34 (58%) and 7 (14%) initiatives respectively and 36 (61%) allowed re-contacting of patients. However, only 31 (53%) had a protocol for communicating genetic results and 23 (39%) had a policy to handle incidental germline mutations. In total, 63% of initiatives are currently sharing data with an additional 10% partially sharing or planning to share. In conclusion, there is currently no uniform approach for collecting data for precision medicine application. GA4GH is actively leading harmonization efforts (e.g. standardized outcome data, toxicity data collection, policies for returning genetic results and strategies for data sharing) to maximize the value of the increasingly complex datasets generated from NGS. Table 1N (%)Number of initiatives59Regional LocationNorth America20 (34)Europe16 (27)Asia7 (12)Australia3 (5)South America3 (5)Intercontinental10 (17)Regional ScopeInstitutional (local)15 (25)Multi-institutional (regional) / National28 (48)European Union4 (7)International10 (17)Unknown2 (3)Patient Samples per Year1-50022 (37)501-500022 (37)>50007 (12)Unknown8 (14)Purpose of testDiagnostic9 (15)Research22 (37)Diagnostic /Research20 (34)Unknown8 (14) Citation Format: Jeremy Lewin, Daniel J. Vis, Mark Lawler, Rachel Liao, Mao Mao, Bin Tean Teh, William Sellers, Robyn Ward, Anamaria Aranha Camargo, Fabrice Andre, Richard Schilsky, Denis Lacombe, Tatsuhiro Shibata, Stephen Fox, Christophe Le Tourneau, William S. Dalton, Bartha Maria Knoppers, Charles Sawyers, Emile E. Voest, Lillian L. Siu. Large variations in clinical and ethical aspects of genomic sequencing initiatives: A Global Alliance for Genomics and Health (GA4GH) survey. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1832.
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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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".