Genetic alterations defining NSCLC subtypes and their therapeutic implications
Bibliographic record
Abstract
Lung cancer is the leading cause of cancer death worldwide, accounting for more deaths than breast, prostate and colon cancer combined. While treatment decisions are determined primarily by stage, therapeutically non small cell lung cancer (NSCLC) has traditionally been treated as a single disease. However, recent findings have led to the recognition of histology and molecular subtypes as important determinants in treatment selection. Identifying the genetic differences that define these molecular and histological subtypes has the potential to impact treatment and as such is currently the focus of much research. Microarray and genomic sequencing efforts have provided unparalleled insight into the genomes of lung cancer subtypes, specifically adenocarcinoma (AC) and squamous cell carcinoma (SqCC), revealing subtype specific genomic alterations and molecular subtypes as well as differences in cell signaling pathways. In this review, we discuss the recurrent genomic alterations characteristic of AC and SqCC (including molecular subtypes), their therapeutic implications and emerging clinical practices aimed at tailoring treatments based on a tumor's molecular alterations with the hope of improving patient response and survival.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".