Development and validation of AI-assisted transcriptomic signatures to personalize adjuvant chemotherapy in patients with pancreatic ductal adenocarcinoma.

BACKGROUND: After surgical resection of pancreatic ductal adenocarcinoma (PDAC), patients are predominantly treated with adjuvant chemotherapy, commonly consisting of gemcitabine-based regimens or the modified FOLFIRINOX regimen (mFFX). While mFFX has been shown to be more effective than gemcitabine-based regimens, it is also associated with higher toxicity. Current treatment decisions are based on patient performance status rather than on the molecular characteristics of the tumor. To address this gap, the goal of this study was to develop drug-specific transcriptomic signatures for personalized chemotherapy treatment. PATIENTS AND METHODS: We used PDAC datasets from preclinical models, encompassing chemotherapy response profiles for the mFFX-regimen components. From them we identified specific gene transcripts associated with chemotherapy response. Three transcriptomic AI-signatures were obtained by combining Independent Component Analysis, Least Absolute Shrinkage and the Selection Operator-Random Forest approach. We integrated a previously developed gemcitabine signature with three newly developed ones. The machine learning strategy employed to enhance these signatures incorporates transcriptomic features from the tumor microenvironment, leading to the development of the Pancreas-View tool ultimately clinically validated in a cohort of 343 patients from the PRODIGE-24/CCTG PA6 trial. RESULTS: Patients who were predicted to be sensitive to the administered drugs (n=164; 47.8%) had longer disease-free survival (DFS) than the other patients. The median DFS in the mFFX sensitive group treated with mFFX was 50.0 months (stratified HR: 0.31; 95% CI, 0.21-0.44; p<0.001) and 33.7 months (stratified HR: 0.40; 95% CI, 0.17-0.59; p<0.001) in the gemcitabine sensitive group when treated with gemcitabine. Comparatively patients with signature predictions unmatched with the treatments (n=86; 25.1%) or those resistant to all drugs (n=93; 27.1%) had shorter DFS (10.6 and 10.8 months, respectively). CONCLUSIONS: This study presents a transcriptome-based tool that was developed using preclinical models and machine learning to accurately predict sensitivity to mFFX and gemcitabine.

Measurement of spectral reflectance and colorimetric properties of Vita shade guides.

Spectrophotometric measurements were done on Vita shade guides. Three shade guides were spectrally analysed. A model was developed for a standard shade guide that can be used repeatedly employing reflectance spectroradiometers.