bioRxivCancer BiologyPreprint
Arrest-associated transcriptional programmes outweigh apoptosis-associated programmes in treatment-naive pancreatic ductal adenocarcinoma
Chukwuemeka A ObuekweiD, Blessing T OgunoyeiD, Aderinsola AdelajaiD, Jude OssaiiD, Abiodun C Adegbesan, Adewunmi AkingbolaiD
Abstract
Background: Pancreatic ductal adenocarcinoma (PDAC) is driven by mutant KRAS, and RAS inhibitors now extend survival in previously treated metastatic disease. Whether treatment shrinks a tumour or only halts its growth depends partly on whether cancer cells die or stop dividing, yet how untreated PDAC cells are balanced between these programmes is unknown. Aims: We aimed to characterise the balance between apoptosis-associated and arrest-associated transcriptional programmes in treatment-naive PDAC and to examine their relationships with tumour subtype, TGM2 expression, tissue organisation, survival and drug sensitivity. Methods and results: We scored 52,999 malignant cells from 18 untreated patients in the Hwang atlas using a transcriptional contrast between genes associated with apoptosis and genes associated with resistance to apoptosis or cell-cycle arrest. In every patient the contrast was shifted toward the arrest-associated side: using equality between the two sides as the dividing threshold, 86.3% of cells with an informative score fell on that side, although the proportion depends on the threshold chosen. The shift recurred in the independent Steele atlas (median 71.6% per sample, range 37.1% to 90.8%). In the discovery cohort, tumours with higher basal-like scores had fewer arrest-side cells, and TGM2 expression was modestly higher on the arrest-associated side in both cohorts. The contrast did not track cell-line drug sensitivity or survival in bulk tumours. Conclusion: Untreated PDAC cells show a reproducible transcriptional bias toward anti-apoptotic and cell-cycle-arrest programmes. Whether this bias predicts cellular responses to treatment remains to be tested.
- organism: human
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- approach: statistical model
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