RESUMO
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.
RESUMO
Pancreatic ductal adenocarcinoma (PDAC) patients are frequently treated by chemotherapy. Even if personalized therapy based on molecular analysis can be performed for some tumors, PDAC regimens selection is still mainly based on patients' performance status and expected efficacy. Therefore, the establishment of molecular predictors of chemotherapeutic efficacy could potentially improve prognosis by tailoring treatments. We have recently developed an RNA-based signature that predicts the efficacy of adjuvant gemcitabine using 38 PDAC primary cell cultures. While demonstrated its efficiency, a significant association with the classical/basal-like PDAC spectrum was observed. We hypothesized that this flaw was due to the basal-like biased phenotype of cellular models used in our strategy. To overcome this limitation, we generated a prospective cohort of 27 consecutive biopsied derived pancreatic organoids (BDPO) and include them in the signature identification strategy. As BDPO's do not have the same biased phenotype as primary cell cultures we expect they can compensate one with each other and cover a broader range of molecular phenotypes. We then obtained an improved signature predicting gemcitabine sensibility that was validated in a cohort of 300 resected PDAC patients that have or have not received adjuvant gemcitabine. We demonstrated a significant association between the improved signature and the overall and disease-free survival in patients predicted as sensitive and treated with adjuvant gemcitabine. We propose then that including BDPO along primary cell cultures represent a powerful strategy that helps to overcome primary cell cultures limitations producing unbiased RNA-based signatures predictive of adjuvant treatments in PDAC.
RESUMO
BACKGROUND: Chemotherapy is the only systemic treatment approved for pancreatic ductal adenocarcinoma (PDAC), with a selection of regimens based on patients' performance status and expected efficacy. The establishment of a potent stratification associated with chemotherapeutic efficacy could potentially improve prognosis by tailoring treatments. PATIENTS AND METHODS: Concomitant chemosensitivity and genome-wide RNA profiles were carried out on preclinical models (primary cell cultures and patient-derived xenografts) derived from patients with PDAC included in the PaCaOmics program (NCT01692873). The RNA-based stratification was tested in a monocentric cohort and validated in a multicentric cohort, both retrospectively collected from resected PDAC samples (67 and 368 patients, respectively). Forty-three (65%) and 203 (55%) patients received adjuvant gemcitabine in the monocentric and the multicentric cohorts, respectively. The relationships between predicted gemcitabine sensitivity and patients' overall survival (OS) and disease-free survival were investigated. RESULTS: The GemPred RNA signature was derived from preclinical models, defining gemcitabine sensitive PDAC as GemPred+. Among the patients who received gemcitabine in the test and validation cohorts, the GemPred+ patients had a higher OS than GemPred- (P = 0.046 and P = 0.00216). In both cohorts, the GemPred stratification was not associated with OS among patients who did not receive gemcitabine. Among gemcitabine-treated patients, GemPred+ patients had significantly higher OS than the GemPred-: 91.3 months [95% confidence interval (CI): 61.2-not reached] versus 33 months (95% CI: 24-35.2); hazard ratio 0.403 (95% CI: 0.221-0.735, P = 0.00216). The interaction test for gemcitabine and GemPred+ stratification was significant (P = 0.0245). Multivariate analysis in the gemcitabine-treated population retained an independent predictive value. CONCLUSION: The RNA-based GemPred stratification predicts the benefit of adjuvant gemcitabine in PDAC patients.
