Point of Care Liquid Biopsy for Cancer Treatment—Early Experience from a Community Center
Bibliographic record
Abstract
Liquid biopsy is rapidly becoming an indispensable tool in the oncologist’s arsenal; however, this technique remains elusive in a publicly funded healthcare system, and real-world evidence is needed to demonstrate utility and feasibility. Here, we describe the first experience of an in-house point of care liquid biopsy program at a Canadian community hospital. A retrospective review of consecutive cases that underwent plasma-based next-generation sequencing (NGS) was conducted. Liquid biopsy was initiated at the discretion of clinicians. Sequencing followed a point of care workflow using the Genexus™ integrated sequencer and the Oncomine precision assay, performed by histotechnologists. Results were reported by the attending pathologist. Eligible charts were reviewed for outcomes of interest, including the intent of the liquid biopsy, results of the liquid biopsy, and turnaround time from blood draw to results available. A total of 124 cases, with confirmed or suspected cancer, underwent liquid biopsy between January 2021 and November 2023. The median turnaround time for liquid biopsy results was 3 business days (range 1–12 days). The sensitivity of liquid biopsies was 71%, compared to tissue testing in cases with matched tissue results available for comparison. Common mutations included EGFR (29%), in 86 lung cancer patients, and PIK3CA (22%), identified in 13 breast cancer patients. Healthcare providers ordered liquid biopsies to inform diagnostic investigations and treatment decisions, and to determine progression or resistance mechanisms, as these reasons often overlapped. This study demonstrates that rapid in-house liquid biopsy using point of care methodology is feasible. The technique facilitates precision treatment and offers many additional advantages for cancer care.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".