Molecular profiling of solid tumors by next-generation sequencing: an experience from a clinical laboratory
Bibliographic record
Abstract
Background: Personalized targeted therapies have transformed management of several solid tumors. Timely and accurate detection of clinically relevant genetic variants in tumor is central to the implementation of molecular targeted therapies. To facilitate precise molecular testing in solid tumors, targeted next-generation sequencing (NGS) assays have emerged as a valuable tool. In this study, we provide an overview of the technical validation, diagnostic yields, and spectrum of variants observed in 3,164 solid tumor samples that were tested as part of the standard clinical diagnostic assessment in an academic healthcare institution over a period of 2 years. Methods: The Ion Ampliseq™ Cancer Hotspot Panel v2 assay (ThermoFisher) that targets ~2,800 COSMIC mutations from 50 oncogenes and tumor suppressor genes was validated, and a total of 3,164 tumor DNA samples were tested in 2 years. A total of 500 tumor samples were tested by the comprehensive panel containing all the 50 genes. Other samples, including 1,375 lung cancer, 692 colon cancer, 462 melanoma, and 135 brain cancer, were tested by tumor-specific targeted subpanels including a few clinically actionable genes. Results: Of 3,164 patient samples, 2,016 (63.7%) tested positive for at least one clinically relevant variant. Of 500 samples tested by a comprehensive panel, 290 had a clinically relevant variant with TP53, KRAS, and PIK3CA being the most frequently mutated genes. The diagnostic yields in major tumor types were as follows: breast (58.4%), colorectal (77.6%), lung (60.4%), pancreatic (84.6%), endometrial (72.4%), ovary (57.1%), and thyroid (73.9%). Tumor-specific targeted subpanels also demonstrated high diagnostic yields: lung (69%), colon (61.2%), melanoma (69.7%), and brain (20.7%). Co-occurrence of mutations in more than one gene was frequently observed. Conclusions: The findings of our study demonstrate the feasibility of integrating an NGS-based gene panel screen as part of a standard diagnostic protocol for solid tumor assessment. High diagnostic rates enable significant clinical impact including improved diagnosis, prognosis, and clinical management in patients with solid tumors.
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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 | 0.019 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".