Comparing genomic landscape of early stage, treatment naïve and late stage, drug resistant EGFR-mutant lung adenocarcinomas
Bibliographic record
Abstract
While, the genomic landscape of early stage, treatment nave lung adenocarcinomas (LUADs) has been described quite elaborately in recent literature, the genomic profile of late stage, drug resistant tumors remains largely unknown. Further, most of the published studies are based on smoker dominated Caucasian cohorts and EGFR-mutant LUAD remains under-represented in them. Despite response rates of upto 70% to EGFR tyrosine kinase inhibitors (TKIs), resistance ensues in most of these EGFRmutant patients, limiting responses to a median of 10-12 months. Thus, to better understand the evolution of these tumors in context of drug resistance, we perform a comparative analysis of the mutational and copy number landscape of early stage, treatment nave vs late stage, resistant tumors. Whole exome sequencing was performed on: (i) 100 tumor sectors from 24 early stage, treatment nave EGFR-mutant LUAD cases. (ii) 81 biopsies from 58 late stage, TKI and chemotherapy resistant cases. Copy number analysis using SNP arrays was performed for a subset of these patients. The significantly higher mutation burden in the late stage, drug resistant tumors elucidated a driver mutation landscape beyond just recurrent TP53 mutations, which was dominated by PIK3CA (14%), RB1 (10%), NF1 (7%) and other rare mutations in EGFR (in 5/58 cases), many of which cooccurred with the T790M mutation. Functional studies validated the oncogenicity of some of these rare mutations in the PI3K/AKT1 pathway. The copy number landscape revealed pervasive, truncal genome doubling events in both cohorts (~80% cases). While comparable fraction of genome was affected by overall copy number gains or losses (copy change >=1) across the two cohorts (49.2% vs 46.3%, P=0.51), significantly higher fraction of genome was affected by amplifications (copy change >=2; 8.7% vs 5%, P=0.02) and loss of heterozygosity (LOH; 33.1% vs 20.6%, P=0.003) in the drug resistant tumors compared to the treatment naive tumors. In summary, our study reveals (i) increased mutation and driver burden with co-occurring resistance
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".