Combination chemotherapy for relapsed small-cell lung cancer—perspective on mechanisms of chemoresistance
Bibliographic record
Abstract
Small cell lung cancer (SCLC) has a dismal prognosis due to early dissemination and aggressive growth. Despite high response rates to initial chemotherapy, SCLC relapses fast and exhibits broad chemoresistance. The JCOG0605 Japanese trial reported increased survival for a regimen combining cisplatin with etoposide and irinotecan compared to topotecan in chemosensitive patients and proposed this treatment as standard chemotherapy. Analysis of the trial data indicates an enrichment of patients with favorable prognosis in the combination chemotherapy arm, questioning the feasibility of this highly aggressive regimen in typical SCLC patients of higher age and afflicted by comorbidities. Considering the modest prolongation of life with current therapies, quality of live should be traded against extension of survival rated in months. Circulating tumor cell (CTC) lines established from relapsed SCLC patients suggest chemoresistance due to formation of large spheroidal multicellular aggregates, termed tumorospheres, which restrict drug access and contain quiescent and hypoxic cells. With the possible exception of metformin, clinical means to eliminate such tumor spheroids are confined to experimental research with cell lines and xenografts, but this new insight into chemoresistance of SCLC discloses entirely new modes of efficient treatment of SCLC.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| 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".