Transcriptome-wide mutation discovery in patients in a phase II clinical trial of first-line erlotinib for clinically selected patients with advanced non-small cell lung cancer
Bibliographic record
Abstract
8102 Background: The purpose of this study was to clinically select a population of patients (pts) likely to benefit from erlotinib and to perform extensive genetic profiling of fresh tissue tumour to identify predictive biomarkers. Methods: Treatment was erlotinib 150 mg p.o. daily until disease progression. Eligibility criteria included: stage IIIB/IV NSCLC; no prior chemo; ECOG ≤2; at least 2 of the following 4 criteria: women, never-smokers, Southeast Asian origin, adenocarcinoma and/or BAC. All pts had baseline serum samples and a fresh frozen tumour biopsy. EGFR and KRAS were sequenced using traditional methods to identify known mutations of erlotinib. Transcribed coding regions of all other expressed genes were sequenced to uncover novel mutations and other genetic features. Results: From Sept-06 to Oct-08, 65 pts have been enrolled. Of these 60 have completed at least 8 wks of erlotinib (or progressed before 8 wks). 49F and 11M; 45 never smokers. PS 0/1/2: 10/39/11. 18 Caucasian and 42 Asian. Pathology: 44 ACA; 3 BAC;1 squamous carcinoma; 13 NSCLC NOS. Responses: PR - 21 (35%); SD - 24 (DCR 75%); PD - 10; NE - 5. 85% did not progress at 8 weeks. 19 of 51 (37%) pre-treatment samples had an EGFR mutation. 10 of the 28 pts with response data (24pts still on drug) had a PR and 7 of these had either an LREA deletion or L858R point mutation. 1 of 3 post-treatment samples developed a T790M mutation on erlotinib. 3 of 51 pts have KRAS mutations. Analysis of over 21 Giga basepairs of aligned transcriptome sequencing data from 20 tumour samples has uncovered 1089 putative non-conservative gene mutations of which 15 are seen in multiple tumours: TUBA1C, EPS8L1, ARID4B, RPL22, C20orf52, CCT8, HLA-DRB5, TRIP6, and PLP2 in 4 tumours, C20orf52 and CTSL1 in 5 tumours, and SERF2, TMEM173, and an uncharacterized gene in 6 tumours. Conclusions: Clinical selection of pts enriches the EGFR mutation positive and KRAS mutation negative population and leads to high rates of non-progression. Full tumour RNA analysis identified several recurrent mutations that may describe mechanisms of erlotinib response and resistance. The discovery of novel mutations in multiple pts suggests patterns that may shed light on lung cancer specific behaviour. [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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".