Drug Resistance Mechanisms in Non-Small Cell Lung Carcinoma
Bibliographic record
Abstract
Lung cancer is the most commonly diagnosed cancer in the world. “Driver” and “passenger” mutations identified in lung cancer indicate that genetics play a major role in the development of the disease, progression, metastasis and response to therapy. Survival rates for lung cancer treatment have remained stagnant at ~15% over the past 40 years in patients with disseminated disease despite advances in surgical techniques, radiotherapy and chemotherapy. Resistance to therapy; either intrinsic or acquired has been a major hindrance to treatment leading to great interest in studies seeking to understand and overcome resistance. Genetic information gained from molecular analyses has been critical in identifying druggable targets and tumor profiles that may be predictors of therapeutic response and mediators of resistance. Mutated or overexpressed epidermal growth factor receptor (EGFR) and translocations in the echinoderm microtubule-associated protein-like 4 (EML4)-anaplastic lymphoma kinase (ALK) genes (EML4-ALK) are examples of genetic aberrations resulting in targeted therapies for both localized and metastatic disease. Positive clinical responses have been noted in patients harboring these genetic mutations when treated with targeted therapies compared to patients lacking these mutations. Resistance is nonetheless a major factor contributing to the failure of targeted agents and standard cytotoxic agents. In this review, we examine molecular mechanisms that are potential drivers of resistance in non-small cell lung carcinoma, the most frequently diagnosed form of lung cancer. The mechanisms addressed include resistance to molecular targeted therapies as well as conventional chemotherapeutics through the activity of multidrug resistance proteins.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".