Using next generation sequencing to detect clinically relevant oncogene mutations in lung cancer
Bibliographic record
Abstract
Introduction: Modern care of patients with lung cancer requires rapid and accurate diagnosis leading to personalized therapies for individual patients based on molecular characteristics of their tumour. Detecting mutations that predict response to drug quickly and accurately is an essential part of this process. Next generation sequencing (NGS) technologies provide an alternative approach for detecting mutated oncogenes in cancer. We hypothesize that NGS is equal if not superior to standard methods for identifying targetable mutations in the EGFR and ALK genes in lung cancer. Methods: DNA and RNA from 38 formalin fixed paraffin embedded lung cancer samples (37 non-small cell lung cancer (NSCLC) and one small cell lung cancer (SCLC)) archived in Diagnostic Services Manitoba were collected and analyzed using gene enrichment methods from Archer Diagnostics followed by sequencing on the Illumina MiSeq NGS machine. Targeted DNA sequencing to detect the EGFR mutation was performed on 19 samples while targeted RNA sequencing was applied to 20 samples to identify the ALK gene rearrangement. The NGS results were compared with and confirmed by current clinical standard molecular tests for EGFR (real-time PCR) and ALK (immunohistochemistry and FISH). Results: Three cases were positive for the EGFR mutation and two other samples harbored the EML4-ALK fusion genes as determined by NGS. The concordance between NGS and real-time PCR for EGFR mutation detection was 88.9%. Additionally, the NGS methodology also provided profiles of other genes commonly mutated in NSCLC including KRAS and TP53. The consistency for ALK fusion testing was 100% between NGS and FISH. Conclusion: This study provides support that NGS is a promising diagnostic tool for mutation detection in NSCLC and holds strong potential for an alternative approach to identifying clinically relevant targets such as EGFR and ALK. Furthermore, NGS provides more information on cancer driven gene mutations than other traditional methods.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".