Plasma-Based Genotyping in Advanced Solid Tumors: A Comprehensive Review
Bibliographic record
Abstract
Molecular genotyping for advanced solid malignancies has transformed the clinical management of patients with metastatic disease. Treatment decisions in a growing number of tumors require knowledge of molecularly driven alterations in order to select optimal targeted therapy. Although genomic testing of tumor tissue is the gold standard for identifying targetable genomic alterations, biopsy samples are often limited or difficult to access. This has paved the way for the development of plasma-based approaches for genomic profiling. Recent advances in the detection of plasma-circulating tumor DNA (ctDNA) have enabled the integration of plasma-based molecular profiling into clinical practice as an alternative or complementary tool for genomic testing in the setting of advanced cancer, to facilitate the identification of driver mutations to guide initial treatment and diagnose resistance. Several guidelines now recommend the use of plasma where tumor tissue is limited to identify a targetable genomic alteration. Current plasma-based assays can evaluate multiple genes in comprehensive panels, and their application in advanced disease will be increasingly incorporated into standard practice. This review focuses on current and future applications of plasma ctDNA-based assays in advanced solid malignancies, while highlighting some limitations in implementing this technology into clinical practice.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".