Rapid access to biomarker data in a community setting: Integration of next-generation sequencing into routine pathologic workflow.
Bibliographic record
Abstract
3143 Background: Biomarker data in the form of next generation sequencing (NGS) are critical to the delivery of precision cancer care. Onsite testing is often limited to large academic centers, requiring smaller community centers to rely on samples send outs. Turnaround time for biomarkers can be lengthy and can adversely affect the delivery of optimal therapy in many tumor types. This study aims to evaluate the feasibility of rapidly delivered comprehensive NGS in a community center using a novel workflow in the laboratory by integrating NGS into the routine immunohistochemistry (IHC) service. Methods: An automated NGS workflow utilizing the Genexus integrated sequencer with the Oncomine precision assay GX (OPA, Thermofisher Scientific), was validated for clinical use and integrated into the routine diagnostic IHC service. During the study period (Oct 2020 – Oct 2021), NGS biomarker data was generated and reported alongside IHC biomarkers where applicable. A retrospective chart review was performed to assess the early experience and performance characteristics of this novel approach to biomarker testing. Results: A total of 578 solid tumor samples underwent genomic profiling. Median turnaround time for biomarker results was 3 business days (IQR 2-5). The majority (n = 481, 83%) of cases were resulted in fewer than 5 business days. Tumor types included lung cancer (n = 310, 54%), melanoma (n = 97, 17%), and colorectal cancer (n = 68, 12%). Specimen types included surgical resections (n = 104, 18%), core biopsies (n = 411, 71%), and cytology specimens (n = 63, 11%). NGS testing detected key driver alterations at expected prevalence rates in respective tumor types; lung EGFR (16%), ALK (3%), RET (1%), melanoma BRAF (43%), colorectal RAS/RAFwild-type (33%), among others. Conclusions: This is the first study demonstrating the clinical feasibility and turnaround time statistics of automated comprehensive NGS performed and interpreted in parallel with diagnostic histopathology and immunohistochemistry in a community setting. This novel approach of integrating biomarkers, IHC, and morphology offers rapid turnaround by removing the need for outsourcing biomarker data. This model could be adopted by other community centers to improve rapid access to biomarker data and therapeutic decision making.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".