Next generation sequencing of pancreatic ductal adenocarcinoma: right or wrong?
Bibliographic record
Abstract
INTRODUCTION: Pancreatic ductal adenocarcinoma (PDAC) has the highest mortality rate of all epithelial malignancies and a paradoxically rising incidence rate. Clinical translation of next generation sequencing (NGS) of tumour and germline samples may ameliorate outcomes by identifying prognostic and predictive genomic and transcriptomic features in appreciable fractions of patients, facilitating enrolment in biomarker-matched trials. Areas covered: The literature on precision oncology is reviewed. It is found that outcomes may be improved across various malignancies, and it is suggested that current issues of adequate tissue acquisition, turnaround times, analytic expertise and clinical trial accessibility may lessen as experience accrues. Also reviewed are PDAC genomic and transcriptomic NGS studies, emphasizing discoveries of promising biomarkers, though these require validation, and the fraction of patients that will benefit from these outside of the research setting is currently unknown. Expert commentary: Clinical use of NGS with PDAC should be used in investigational contexts in centers with multidisciplinary expertise in cancer sequencing and pancreatic cancer management. Biomarker directed studies will improve our understanding of actionable genomic variation in PDAC, and improve outcomes for this challenging disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".