RESUMO
BACKGROUND: After surgical resection of pancreatic ductal adenocarcinoma (PDAC), patients are predominantly treated with adjuvant chemotherapy, commonly consisting of gemcitabine (GEM)-based regimens or the modified FOLFIRINOX (mFFX) regimen. While mFFX regimen has been shown to be more effective than GEM-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 artificial intelligence signatures were obtained by combining independent component analysis and the least absolute shrinkage and selection operator-random forest approach. We integrated a previously developed GEM 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 hazard ratio (HR) 0.31, 95% confidence interval (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 GEM-sensitive group when treated with GEM. 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 GEM.
RESUMO
BACKGROUND: Neoadjuvant immunotherapy emerges as a promising strategy for patients with localized colon cancer (CC) harboring microsatellite instability/mismatch repair deficiency (MSI/dMMR). The aim of this study is to evaluate the concordance between clinical cTN stage assessed by preoperative computed tomography (CT) scan and pTN stage of MSI/dMMR CC. PATIENTS AND METHODS: Consecutive patients diagnosed for localized MSI/dMMR CC and treated with upfront surgery between 2013 and 2022 in two French centers were eligible. Two independent radiologists, blinded to pathological findings, reviewed all preoperative CT scans and assessed cTN stage, with a third radiologist reviewing discordant cases. Radiological predictive diagnostic accuracy for pT4 and pN+ (N+ = N1 or N2) were calculated. RESULTS: One hundred and thirteen patients were included (right CCs = 79%). CT scan diagnostic performances for pT4 were sensitivity (Se) = 33.3%; specificity (Sp) = 94.0%; positive predictive value (PPV) = 66.7%; and negative predictive value (NPV) = 79.6% and for pN+ were Se = 70.3%; Sp = 59.2%; PPV = 45.6%; and NPV = 80.4%. When pT-pN were combined, 37.5% of tumors identified as cT4 and/or cN+ were actually pT1-3 and pN0, and 23.1% of the pT4 and pN+ population was not identified as such radiologically. CONCLUSION: The ability of preoperative CT scan to predict pT and pN stages is limited for localized MSI/dMMR CCs. Reassessing neoadjuvant strategies' benefit-risk balance in this population is needed.