Esophageal carcinoma: Towards targeted therapies
Bibliographic record
Abstract
BACKGROUND: Patients with esophageal cancer are confronted with high mortality rates. Whether it is esophageal squamous cell carcinoma (ESCC) or esophageal adenocarcinoma (EAC), patients usually present at advanced stages, with treatment options traditionally involving chemotherapy in metastatic settings. With the comprehensive genomic characterization of esophageal cancers, targeted therapies are gaining interest and agents such as ramucirumab, trastuzumab and pembrolizumab are already being used for the treatment of EAC. CONCLUSIONS: Pembrolizumab has recently been FDA-approved for PD-L1 positive, locally advanced or metastatic ESCC. Despite comprehensive molecular characterization, however, available targed therapies for ESCC are still lagging behind. Herein, we discuss current trends towards more targeted therapies in esophageal cancers, taking into consideration unique features of ESCCs and EACs. Patients progressing on standard therapies should be subjected to genomic profiling and considered for clinical trials aimed at testing targeted therapies. Future targeted therapies may include CDK4/6 inhibitors, PARP inhibitors and inhibitors targeting the NRF2 and Wnt signaling pathways. Ultimately, optimized biomarker assays and next generation sequencing platforms may allow for the identification of subcategories of ESCC and EAC patients that will benefit from selective targeted therapies and/or combinations thereof.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".