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Predictive Values of Blood-Based RNA Signatures for the Gemcitabine Response in Advanced Pancreatic Cancer.

ABSTRACT: Pancreatic ductal adenocarcinoma (PDAC) is expected to be the second cause of cancer death by 2022. For nearly 80% of patients, diagnosis occurs at an advanced, nonsurgical stage, making such patients incurable. Gemcitabine is still an important component in PDAC treatment and is most often used as a backbone to test new targeted therapies and there is, to date, no routine biomarker to predict its efficacy. Samples from a phase III randomized trial were used to develop through a large approach based on blood-based liquid biopsy, transcriptome profiling, and machine learning, a nine gene predictive signature for gemcitabine sensitivity. Patients with a positive test (41.6%) had a significantly longer progression free survival (PFS) (3.8 months vs. 1.9 months p = 0.03) and a longer overall survival (OS) (14.5 months vs. 5.1, p < 0.0001). In multivariate analyses, this signature was independently associated with PFS (HR = 0.5 (0.28-0.9) p = 0.025) and OS (HR = 0.39 (0.21-0.7) p = 0.002).

SUBMITTER: Piquemal D

PROVIDER: S-EPMC7692046 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Predictive Values of Blood-Based RNA Signatures for the Gemcitabine Response in Advanced Pancreatic Cancer.

Piquemal David D Noguier Florian F Pierrat Fabien F Bruno Roman R Cros Jerome J

Cancers 20201030 11

Pancreatic ductal adenocarcinoma (PDAC) is expected to be the second cause of cancer death by 2022. For nearly 80% of patients, diagnosis occurs at an advanced, nonsurgical stage, making such patients incurable. Gemcitabine is still an important component in PDAC treatment and is most often used as a backbone to test new targeted therapies and there is, to date, no routine biomarker to predict its efficacy. Samples from a phase III randomized trial were used to develop through a large approach b ...[more]

PMID: 33143297

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Project description:A clear contradiction exists between cytotoxic in-vitro studies demonstrating effectiveness of Gemcitabine to curtail pancreatic cancer and in-vivo studies failing to show Gemcitabine as an effective treatment. The outcome of chemotherapy in metastatic stages, where surgery is no longer viable, shows a 5-year survival <5%. It is apparent that in-vitro experiments, no matter how well designed, may fail to adequately represent the complex in-vivo microenvironmental and phenotypic characteristics of the cancer, including cell proliferation and apoptosis. We evaluate in-vitro cytotoxic data as an indicator of in-vivo treatment success using a mathematical model of tumor growth based on a dimensionless formulation describing tumor biology. Inputs to the model are obtained under optimal drug exposure conditions in-vitro. The model incorporates heterogeneous cell proliferation and death caused by spatial diffusion gradients of oxygen/nutrients due to inefficient vascularization and abundant stroma, and thus is able to simulate the effect of the microenvironment as a barrier to effective nutrient and drug delivery. Analysis of the mathematical model indicates the pancreatic tumors to be mostly resistant to Gemcitabine treatment in-vivo. The model results are confirmed with experiments in live mice, which indicate uninhibited tumor proliferation and metastasis with Gemcitabine treatment. By extracting mathematical model parameter values for proliferation and death from monolayer in-vitro cytotoxicity experiments with pancreatic cancer cells, and simulating the effects of spatial diffusion, we use the model to predict the drug response in-vivo, beyond what would have been expected from sole consideration of the cancer intrinsic resistance. We conclude that this integrated experimental/computational approach may enhance understanding of pancreatic cancer behavior and its response to various chemotherapies, and, further, that such an approach could predict resistance based on pharmacokinetic measurements with the goal to maximize effective treatment strategies.

Project description:BackgroundPancreatic ductal adenocarcinoma (PDAC) is a lethal cancer that is characterized by its poor prognosis, rapid progression and development of drug resistance. Chemotherapy is a vital treatment option for most of PDAC patients. Stratification of PDAC patients, who would have a higher likelihood of responding to chemotherapy, could facilitate treatment selection and patient management.MethodsA quantitative proteomic study was performed to characterize the protein profiles in the plasma of PDAC patients undergoing chemotherapy to determine if specific biomarkers could be used to predict likelihood of therapeutic response.ResultsBy comparing the plasma proteome of the PDAC patients with positive therapeutic response and longer overall survival (Good-responders) to those who did not respond as well with shorter survival time (Limited-responders), we identified differential proteins and protein variants that could effectively segregate Good-responders from Limited-responders. Functional clustering and pathway analysis further suggested that many of these differential proteins were involved in pancreatic tumorigenesis. Four proteins, including vitamin-K dependent protein Z (PZ), sex hormone-binding globulin (SHBG), von Willebrand factor (VWF) and zinc-alpha-2-glycoprotein (AZGP1), were further evaluated as single or composite predictive biomarker with/without inclusion of CA 19-9. A composite biomarker panel that consists of PZ, SHBG, VWF and CA 19-9 demonstrated the best performance in distinguishing Good-responders from Limited-responders.ConclusionBased on the cohort investigated, our data suggested that systemic proteome alterations involved in pathways associated with inflammation, immunoresponse, coagulation and complement cascades may be reporters of chemo-treatment outcome in PDAC patients. For the majority of the patients involved, the panel consisting of PZ, SHBG, VWF and CA 19-9 was able to segregate Good-responders from Limited-responders effectively. Our data also showed that dramatic fluctuations of biomarker concentration in the circulating system of a PDAC patient, which might result from biological heterogeneity or confounding complications, could diminish the performance of a biomarker. Categorization of PDAC patients in terms of their tumor stages and histological types could potentially facilitate patient stratification for treatment.

Project description:BackgroundPancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies. While the extracellular matrix component plays an integral role in PDAC pathogenesis and mediating chemoresistance, its role in predicting response to chemotherapy in patients with PDAC remains unclear.MethodsWe performed a systematic biomarker discovery by analysing genome-wide transcriptomic profiling data from 423 patients (GSE71729, GSE21501 and The Cancer Genome Atlas [TCGA]) for predicting overall survival (OS). This was subsequently validated in two independent clinical cohorts of 270 patients with PDAC (training cohort, n = 121, and validation cohort, n = 149). In addition, we investigated endoscopic ultrasound-fine needle aspiration biopsy specimens from 51 patients with PDAC with an unresectable cancer for predicting therapeutic response to gemcitabine-based therapy.ResultsAfter rigorous bioinformatic analysis, we identified laminin γ2 (LAMC2) to be a significant prognostic factor in all three PDAC data sets (GSE71729: hazard ratio [HR] = 2.04, P = 0.002; GSE21501: HR = 2.17, P = 0.031; TCGA: HR = 2.57, P < 0.001). High LAMC2 expression in patients with PDAC was associated with a significantly poor OS and relapse-free survival in both the training (P < 0.001, P < 0.001) and validation cohorts (P = 0.001, P = 0.026). More importantly, LAMC2 expression robustly identified patients with PDAC and unresectable disease and those who responded to gemcitabine-based therapy (area under the curve = 0.79; 95% confidence interval [CI], 0.65-0.89). The univariate logistic regression analysis revealed that high LAMC2 expression was the only factor that predicted poor response to gemcitabine in patients with PDAC (odds ratio = 4.90; 95% CI, 1.45-16.6; P = 0.011).ConclusionWe conclude that LAMC2 is a novel prognostic and predictive biomarker for gemcitabine-based therapy in both the adjuvant and palliative setting; which could have significant impact on precision and individualised treatment of patients with PDAC.

Dataset Information

Predictive Values of Blood-Based RNA Signatures for the Gemcitabine Response in Advanced Pancreatic Cancer.

Publications

Predictive Values of Blood-Based RNA Signatures for the Gemcitabine Response in Advanced Pancreatic Cancer.

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