Rapid point of care NGS in colorectal cancer.
Bibliographic record
Abstract
172 Background: Next generation sequencing (NGS) is the laboratory cornerstone of precision oncology treatment. In advanced colorectal cancer (CRC), current guidelines recommend testing RAS, BRAF and MMR biomarkers as standard of care. The added value of comprehensive genomic profiling is so far unclear. Traditional NGS operations are complicated, requiring specialized equipment and personnel. In many jurisdictions, cancer patients are treated in publicly-funded community hospitals, where NGS is not typically utilized and access to testing via send-out services is associated with lengthy turnaround times. Here, we have validated and implemented one of the world's first "point of care" NGS services. Our early experience on NGS implementation and impact in CRC patients is described. Methods: All NGS studies were performed using the Oncomine Precision Assay (OPA) on the genexus integrated sequencer. NGS was performed at the request of the treating physician. All NGS was performed in a local community pathology lab by histotechnologists, simultaneously responsible for IHC testing (such as MMR) and interpreted by anatomic pathologists in conjunction with routine diagnostic pathology services. Retrospective chart review was performed for all patients undergoing sequencing studies and key data, including turnaround time and NGS findings were extracted from the electronic medical record for analysis. Results: A total of 51 cases with CRC were tested using point of care NGS from November 2020-August 2021, initiated by treating physicians. The median turnaround time for results was 3 days. Oncogenic driver events were identified in 46 (90%) cases, including canonical mutations in KRAS, NRAS and BRAF (Table). Actionable mutations were identified in 13 (25%) samples that would not have been identified with single-gene testing. Conclusions: Here, we show that comprehensive NGS can reveal occult resistance mechanisms to standard therapy and identify actionable biomarkers in a substantial proportion of patients with CRC. NGS added valuable information compared to guideline-recommended testing standards. Our study demonstrates that local testing can have rapid turnaround times. To our knowledge, this is the first report of “point of care” NGS in CRC. Further follow up is needed to explore the utility of these expanded roles for NGS testing. [Table: see text]
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".