Elucidating Genomic Characteristics of Lung Cancer Progression from In Situ to Invasive Adenocarcinoma
Bibliographic record
Abstract
To examine the diversity of somatic alterations and clonal evolution according to aggressiveness of disease, nineteen tumor-blood pairs of 'formerly bronchiolo-alveolar carcinoma (BAC)' which had been reclassified into preinvasive lesion (adenocarcinoma in situ; AIS), focal invasive lesion (minimally invasive adenocarcinoma; MIA), and invasive lesion (lepidic predominant adenocarcinoma; LPA and non-lepidic predominant adenocarcinoma; non-LPA) according to IASLC/ATS/ERS 2011 classification were explored by whole exome sequencing. Several distinct somatic alterations were observed compare to the lung adenocarcinoma study from the Cancer Genome Atlas (TCGA). There were higher numbers of tumors with significant APOBEC mutation fold enrichment (73% vs. 58% TCGA). The frequency of KRAS mutations was lower in our study (5% vs. 32% TCGA), while a higher number of mutations of RNA-splicing genes, RBM10 and U2AF1, were found (37% vs. 11% TCGA). We found neither mutational pattern nor somatic copy number alterations that were specific to AIS/MIA. We demonstrated that clonal cell fraction was the only distinctive feature that discriminated LPA/non-LPA from AIS/MIA. The broad range of clonal frequency signified a more branched clonal evolution at the time of diagnosis. Assessment of tumor clonal cell fraction might provide critical information for individualized therapy as a prognostic factor, however this needs further study.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".