scholarly journals [18F]FDG PET/MRI enables early chemotherapy response prediction in pancreatic ductal adenocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Felix N. Harder ◽  
Friederike Jungmann ◽  
Georgios A. Kaissis ◽  
Fabian K. Lohöfer ◽  
Sebastian Ziegelmayer ◽  
...  
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Fdg Pet ◽  
Response Prediction ◽  
Clinical Impact ◽  
Ductal Adenocarcinoma ◽  
Chemotherapy Response ◽  
Exact Test ◽  
Palliative Intent ◽  
Tumor Glycolysis ◽  
Binary Features

Abstract Purpose In this prospective exploratory study, we evaluated the feasibility of [18F]fluorodeoxyglucose ([18F]FDG) PET/MRI-based chemotherapy response prediction in pancreatic ductal adenocarcinoma at two weeks upon therapy onset. Material and methods In a mixed cohort, seventeen patients treated with chemotherapy in neoadjuvant or palliative intent were enrolled. All patients were imaged by [18F]FDG PET/MRI before and two weeks after onset of chemotherapy. Response per RECIST1.1 was then assessed at 3 months [18F]FDG PET/MRI-derived parameters (MTV50%, TLG50%, MTV2.5, TLG2.5, SUVmax, SUVpeak, ADCmax, ADCmean and ADCmin) were assessed, using multiple t-test, Man–Whitney-U test and Fisher’s exact test for binary features. Results At 72 ± 43 days, twelve patients were classified as responders and five patients as non-responders. An increase in ∆MTV50% and ∆ADC (≥ 20% and 15%, respectively) and a decrease in ∆TLG50% (≤ 20%) at 2 weeks after chemotherapy onset enabled prediction of responders and non-responders, respectively. Parameter combinations (∆TLG50% and ∆ADCmax or ∆MTV50% and ∆ADCmax) further improved discrimination. Conclusion Multiparametric [18F]FDG PET/MRI-derived parameters, in particular indicators of a change in tumor glycolysis and cellularity, may enable very early chemotherapy response prediction. Further prospective studies in larger patient cohorts are recommended to their clinical impact.

scholarly journals Plasma Metabolome Profiling Identifies Metabolic Subtypes of Pancreatic Ductal Adenocarcinoma

Cells ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 1821
Author(s):  
Ujjwal Mukund Mahajan ◽  
Ahmed Alnatsha ◽  
Qi Li ◽  
Bettina Oehrle ◽  
Frank-Ulrich Weiss ◽  
...  
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Enrichment Analysis ◽  
Response Prediction ◽  
Ductal Adenocarcinoma ◽  
Pathway Enrichment Analysis ◽  
Metabolome Analysis ◽  
Dismal Prognosis ◽  
Chemotherapeutic Response ◽  
Plasma Metabolome ◽  
Metabolome Profiling

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers. Developing biomarkers for early detection and chemotherapeutic response prediction is crucial to improve the dismal prognosis of PDAC patients. However, molecular cancer signatures based on transcriptome analysis do not reflect intratumoral heterogeneity. To explore a more accurate stratification of PDAC phenotypes in an easily accessible matrix, plasma metabolome analysis using MxP® Global Profiling and MxP® Lipidomics was performed in 361 PDAC patients. We identified three metabolic PDAC subtypes associated with distinct complex lipid patterns. Subtype 1 was associated with reduced ceramide levels and a strong enrichment of triacylglycerols. Subtype 2 demonstrated increased abundance of ceramides, sphingomyelin and other complex sphingolipids, whereas subtype 3 showed decreased levels of sphingolipid metabolites in plasma. Pathway enrichment analysis revealed that sphingolipid-related pathways differ most among subtypes. Weighted correlation network analysis (WGCNA) implied PDAC subtypes differed in their metabolic programs. Interestingly, a reduced expression among related pathway genes in tumor tissue was associated with the lowest survival rate. However, our metabolic PDAC subtypes did not show any correlation to the described molecular PDAC subtypes. Our findings pave the way for further studies investigating sphingolipids metabolisms in PDAC.

scholarly journals Clinical Impact of 18F-FDG PET/CT in the Diagnostic Workup of Pancreatic Ductal Adenocarcinoma: A Systematic Review

Diagnostics ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 1042
Author(s):  
Annachiara Arnone ◽  
Riccardo Laudicella ◽  
Federico Caobelli ◽  
Priscilla Guglielmo ◽  
Marianna Spallino ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Positron Emission Tomography ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Fdg Pet ◽  
Emission Tomography ◽  
Ductal Adenocarcinoma ◽  
Positron Emission ◽  
Pet Ct ◽  
18F Fdg ◽  
Fdg Pet Ct

In this review, the performance of fluorodeoxyglucose (FDG)-positron emission tomography (PET)/computed tomography (CT) in the diagnostic workup of pancreatic ductal adenocarcinoma (PDAC) is evaluated. A comprehensive literature search up to September 2020 was performed, selecting studies with the presence of: sample size ≥10 patients and index test (i.e., “FDG” or “18F-FDG” AND “pancreatic adenocarcinoma” or “pancreas cancer” AND “PET” or “positron emission tomography”). The methodological quality was evaluated using the revised quality assessment of diagnostic accuracy studies (QUADAS-2) tool and presented according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Basic data (authors, year of publication, country and study design), patients’ characteristics (number of enrolled subjects and age), disease phase, type of treatment and grading were retrieved. Forty-six articles met the adopted research criteria. The articles were divided according to the considered clinical context. Namely, besides conventional anatomical imaging, such as computed tomography (CT) and magnetic resonance imaging (MRI), molecular imaging with FDG PET/CT is an important tool in PDAC, for all disease stages. Further prospective studies will be necessary to confirm the cost-effectiveness of such imaging techniques by testing its real potential improvement in the clinical management of PDAC.

scholarly journals Pancreatic Ductal Adenocarcinoma (PDAC) Organoids: The Shining Light at the End of the Tunnel for Drug Response Prediction and Personalized Medicine

Cancers ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2750
Author(s):  
Pierre-Olivier Frappart ◽  
Thomas G. Hofmann
Keyword(s):  
Personalized Medicine ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Drug Response ◽  
Treatment Options ◽  
Response Prediction ◽  
Ductal Adenocarcinoma ◽  
Chemotherapy Treatment ◽  
The Past ◽  
Therapeutic Tools ◽  
And Personalized Medicine

Pancreatic ductal adenocarcinoma (PDAC) represents 90% of pancreatic malignancies. In contrast to many other tumor entities, the prognosis of PDAC has not significantly improved during the past thirty years. Patients are often diagnosed too late, leading to an overall five-year survival rate below 10%. More dramatically, PDAC cases are on the rise and it is expected to become the second leading cause of death by cancer in western countries by 2030. Currently, the use of gemcitabine/nab-paclitaxel or FOLFIRINOX remains the standard chemotherapy treatment but still with limited efficiency. There is an urgent need for the development of early diagnostic and therapeutic tools. To this point, in the past 5 years, organoid technology has emerged as a revolution in the field of PDAC personalized medicine. Here, we are reviewing and discussing the current technical and scientific knowledge on PDAC organoids, their future perspectives, and how they can represent a game change in the fight against PDAC by improving both diagnosis and treatment options.

scholarly journals Integration of Bioinformatics Resources Reveals the Therapeutic Benefits of Gemcitabine and Cell Cycle Intervention in SMAD4-Deleted Pancreatic Ductal Adenocarcinoma

Genes ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 766 ◽  
Author(s):  
Yao-Yu Hsieh ◽  
Tsang-Pai Liu ◽  
Chia-Jung Chou ◽  
Hsin-Yi Chen ◽  
Kuen-Haur Lee ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Gene Deletion ◽  
Transforming Growth Factor ◽  
Ductal Adenocarcinoma ◽  
Gene Copy ◽  
Chemotherapy Response ◽  
Specific Cell ◽  
Smad4 Gene ◽  
Therapeutic Benefits

Pancreatic ductal adenocarcinoma (PDAC) is the most common and aggressive type of pancreatic cancer. The five-year survival rate of PDAC is very low (less than 8%), which is associated with the late diagnosis, high metastatic potential, and resistance to therapeutic agents. The identification of better prognostic or therapeutic biomarker may have clinical benefits for PDAC treatment. SMAD4, a central mediator of transforming growth factor beta (TGFβ) signaling pathway, is considered a tumor suppressor gene. SMAD4 inactivation is frequently found in PDAC. However, its role in prognosis and therapeutics of PDAC is still unclear. In this study, we applied bioinformatics approaches, and integrated publicly available resources, to investigate the role of SMAD4 gene deletion in PDAC. We found that SMAD4 deletion was associated with poorer disease-free, but not overall, survival in PDAC patients. Cancer hallmark enrichment and pathway analysis suggested that the upregulation of cell cycle-related genes in SMAD4-deleted PDAC. Chemotherapy response profiling of PDAC cell lines and patient-derived organoids revealed that SMAD4-deleted PDAC was sensitive to gemcitabine, the first-line treatment for PDAC, and specific cell cycle-targeting drugs. Taken together, our study provides an insight into the prognostic and therapeutic roles of SMAD4 gene deletion in PDAC, and SMAD4 gene copy numbers may be used as a therapeutic biomarker for PDAC treatment.

scholarly journals Acinar transformed ductal cells exhibit differential mucin expression in a tamoxifen-induced pancreatic ductal adenocarcinoma mouse model

Biology Open ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. bio052878
Author(s):  
Kavita Mallya ◽  
Dhanya Haridas ◽  
Parthasarathy Seshacharyulu ◽  
Ramesh Pothuraju ◽  
Wade M. Junker ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Mouse Model ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Intraepithelial Neoplasia ◽  
Ductal Adenocarcinoma ◽  
Chemotherapy Response ◽  
Pancreatic Intraepithelial Neoplasia ◽  
Ductal Cells ◽  
Mucin Expression ◽  
Acinar To Ductal Metaplasia

ABSTRACTPancreatic cancer (PC) is acquired postnatally; to mimic this scenario, we developed an inducible KrasG12D; Ptf1a-CreER™ (iKC) mouse model, in which Kras is activated postnatally at week 16 upon tamoxifen (TAM) administration. Upon TAM treatment, iKC mice develop pancreatic intraepithelial neoplasia (PanIN) lesions and PC with metastasis at the fourth and fortieth weeks, respectively, and exhibited acinar-to-ductal metaplasia (ADM) and transdifferentiation. Kras activation upregulated the transcription factors Ncoa3, p-cJun and FoxM1, which in turn upregulated expression of transmembrane mucins (Muc1, Muc4 and Muc16) and secretory mucin (Muc5Ac). Interestingly, knockdown of KrasG12D in multiple PC cell lines resulted in downregulation of MUC1, MUC4, MUC5AC and MUC16. In addition, iKC mice exhibited ADM and transdifferentiation. Our results show that the iKC mouse more closely mimics human PC development and can be used to investigate pancreatic ductal adenocarcinoma (PDAC) biomarkers, early onset of PDAC, and ADM. The iKC model can also be used for preclinical strategies such as targeting mucin axis alone or in combination with neo-adjuvant, immunotherapeutic approaches and to monitor chemotherapy response.

The Role of 18F-FDG PET/CT and PET/MRI in Pancreatic Ductal Adenocarcinoma

2017 ◽  
Vol 43 (2) ◽  
pp. 415-434 ◽  
Author(s):  
Randy Yeh ◽  
Laurent Dercle ◽  
Ishan Garg ◽  
Zhen Jane Wang ◽  
David M. Hough ◽  
...  
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Fdg Pet ◽  
Ductal Adenocarcinoma ◽  
Pet Ct ◽  
18F Fdg ◽  
Fdg Pet Ct

scholarly journals A machine learning algorithm predicts molecular subtypes in pancreatic ductal adenocarcinoma with differential response to gemcitabine-based versus FOLFIRINOX chemotherapy

2019 ◽  
Author(s):  
Georgios Kaissis ◽  
Sebastian Ziegelmayer ◽  
Fabian Lohöfer ◽  
Katja Steiger ◽  
Hana Algül ◽  
...  
Keyword(s):  
Machine Learning ◽  
Overall Survival ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Median Overall Survival ◽  
Molecular Subtypes ◽  
Supervised Machine Learning ◽  
Ductal Adenocarcinoma ◽  
Chemotherapy Response ◽  
Response To Chemotherapy ◽  
Sensitivity Specificity

AbstractPurposeDevelopment of a supervised machine-learning model capable of predicting clinically relevant molecular subtypes of pancreatic ductal adenocarcinoma (PDAC) from diffusion-weighted-imaging-derived radiomic features.MethodsThe retrospective observational study assessed 55 surgical PDAC patients. Molecular subtypes were defined by immunohistochemical staining of KRT81. Tumors were manually segmented and 1606 radiomic features were extracted withPyRadiomics. A gradient-boosted-tree algorithm (XGBoost) was trained on 70% of the patients (N=28) and tested on 30% (N=17) to predict KRT81+ vs. KRT81-tumor subtypes. The average sensitivity, specificity and ROC-AUC value were calculated. Chemotherapy response was assessed stratified by subtype. Radiomic feature importance was ranked.ResultsThe mean±STDEV sensitivity, specificity and ROC-AUC were 0.90±0.07, 0.92±0.11, and 0.93±0.07, respectively. Patients with a KRT81+ subtype experienced significantly diminished median overall survival compared to KRT81-patients (7.0 vs. 22.6 months, HR 1.44, log-rank-test P=<0.001) and a significantly improved response to gemcitabine-based chemotherapy over FOLFIRINOX (10.14 vs. 3.8 months median overall survival, HR 0.85, P=0.037) compared to KRT81-patients, who responded significantly better to FOLFIRINOX over gemcitabine-based treatment (30.8 vs. 13.4 months median overall survival, HR 0.88, P=0.027).ConclusionsThe machine-learning based analysis of radiomic features enables the prediction of subtypes of PDAC, which are highly relevant for overall patient survival and response to chemotherapy.

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