Apparent Diffusion Coefficient (ADC) predicts therapy response in pancreatic ductal adenocarcinoma

Trajkovic‐Arsic, Marija; Heid, Irina; Steiger, Katja; Gupta, Aayush; Fingerle, Alexander A.; Wörner, C; Teichmann, Nicole; Sengkwawoh-Lueong, S.; Wenzel, Patrick; Beer, Ambros J.; Espósito, Irene; Braren, Rickmer; Siveke, Jens T.

doi:10.1038/s41598-017-16826-z

Cited by 30 publications

(36 citation statements)

References 31 publications

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“…Post mortem analyses of terminal stage PDAC specimens have shown higher tumor cellularity compared to resectable PDAC specimens, which likely represent earlier tumor development stages (14). In line with this observation we previously demonstrated that higher regional tumor cellularity identified in PDAC resection specimens was associated with a significantly worse overall survival and that the pre-operative DW-MRI-derived ADC parameter could serve as a non-invasive marker thereof (15,16). Upholding these findings, the current analysis identified (12).…”

Section: Discussionsupporting

confidence: 84%

A prospectively validated machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma

Kaissis

Ziegelmayer

Lohöfer

et al. 2019

Preprint

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Purpose: To develop a supervised machine learning algorithm capable of predicting above vs. below-median overall survival from medical imaging-derived radiomic features in a cohort of patients with pancreatic ductal adenocarcinoma (PDAC). Materials and Methods: 102 patients with histopathologically proven PDAC were retrospectively assessed as the training cohort and 30 prospectively enrolled patients served as the external validation cohort. Tumors were segmented in pre-operative diffusion weighted-(DW)-MRI derived ADC maps and radiomic features were extracted. A Random Forest machine learning algorithm was fit to the training cohort and tested in the external validation cohort. The histopathological subtype of the tumor samples was assessed by immunohistochemistry in 21/30 patients of the external validation cohort. Individual radiomic feature importance was evaluated.Results: The machine learning algorithm achieved a sensitivity of 87% and a specificity of 80% (ROC-AUC 90%) for the prediction of above-vs. below-median survival on the unseen data of the external validation cohort. Heterogeneity-related features were highly ranked by the model.Of the 21 patients for whom the histopathological subtype was determined, 8/9 patients predicted by the model to experience below-median overall survival exhibited the quasimesenchymal subtype, while 11/12 patients predicted to experience above-median survival exhibited a non-quasi-mesenchymal subtype (Fisher's exact test P<0.001).Conclusion: The application of machine-learning to the radiomic analysis of DW-MRI-derived ADC maps allowed the prediction of overall survival with high diagnostic accuracy in a prospectively collected cohort. The high overlap of clinically relevant histopathological subtypes with model predictions underlines the potential of quantitative imaging workflows in pre-operative subtyping and risk assessment in PDAC.

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Section: Discussionsupporting

confidence: 84%

A prospectively validated machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma

Kaissis

Ziegelmayer

Lohöfer

et al. 2019

Preprint

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“…Slug-YFP mice were generously provided by Robert A. Weinberg, Whitehead Institute for Biomedical Research, Cambridge, MA. For the in vivo treatment, animals received refametinib (BAY86-9766) as published previously [15]. Primary tumors were minced and digested with collagenase (STEMCELL Technologies, 07902).…”

Section: Methodsmentioning

confidence: 99%

MEK Inhibition Targets Cancer Stem Cells and Impedes Migration of Pancreatic Cancer CellsIn VitroandIn Vivo

Walter

Tiwary

Trajkovic‐Arsic

et al. 2019

Stem Cells International

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Pancreatic ductal adenocarcinoma (PDAC) remains a devastating disease with a very poor prognosis. At the same time, its incidence is on the rise, and PDAC is expected to become the second leading cause of cancer-related death by 2030. Despite extensive work on new therapeutic approaches, the median overall survival is only 6-12 months after diagnosis and the 5-year survival is less than 7%. While pancreatic cancer is particularly difficult to treat, patients usually succumb not to the growth of the primary tumor, but to extensive metastasis; therefore, strategies to reduce the migratory and metastatic capacity of pancreatic cancer cells merit close attention. The vast majority of pancreatic cancers harbor RAS mutations. The outstanding relevance of the RAS/MEK/ERK pathway in pancreatic cancer biology has been extensively shown previously. Due to their high dependency on Ras mutations, pancreatic cancers might be particularly sensitive to inhibitors acting downstream of Ras. Herein, we use a genetically engineered mouse model of pancreatic cancer and primary pancreatic cancer cells were derived from this model to demonstrate that small-molecule MEK inhibitors functionally abrogate cancer stem cell populations as demonstrated by reduced sphere and organoid formation capacity. Furthermore, we demonstrate that MEK inhibition suppresses TGFβ-induced epithelial-to-mesenchymal transition and migration in vitro and ultimately results in a highly significant reduction in circulating tumor cells in mice.

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“…Ptf1a wt/cre ; Kras wt/LSL-KrasG12D ; Tp53 fl/fl (CKP) mice developing spontaneous aggressive PDAC were generated as previously described 42 and treated for 48 h with the MEK inhibitor refametinib 43 . Patient-derived xenografts were generated by subcutaneous transplantation of resected PDAC tumors or from purified circulating tumorigenic cancer stem cells in NOD/SCID/IL2y-mice and propagated in NMRI:nu/nu mice after engraftment 23 .…”

Section: Mice and Treatmentsmentioning

confidence: 99%

TFEB-mediated lysosomal biogenesis and lysosomal drug sequestration confer resistance to MEK inhibition in pancreatic cancer

Zhao

Dierichs

et al. 2020

Cell Death Discov.

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Oncogenic KRAS mutations are encountered in more than 90% of pancreatic ductal adenocarcinomas. MEK inhibition has failed to procure any clinical benefits in mutant RAS-driven cancers including pancreatic ductal adenocarcinoma (PDAC). To identify potential resistance mechanisms underlying MEK inhibitor (MEKi) resistance in PDAC, we investigated lysosomal drug accumulation in PDAC models both in vitro and in vivo. Mouse PDAC models and human PDAC cell lines as well as human PDAC xenografts treated with the MEK inhibitor trametinib or refametinib led to an enhanced expression of lysosomal markers and enrichment of lysosomal gene sets. A time-dependent, increase in lysosomal content was observed upon MEK inhibition. Strikingly, there was a strong activation of lysosomal biogenesis in cell lines of the classical compared to the basal-like molecular subtype. Increase in lysosomal content was associated with nuclear translocation of the Transcription Factor EB (TFEB) and upregulation of TFEB target genes. siRNA-mediated depletion of TFEB led to a decreased lysosomal biogenesis upon MEK inhibition and potentiated sensitivity. Using LC-MS, we show accumulation of MEKi in the lysosomes of treated cells. Therefore, MEK inhibition triggers lysosomal biogenesis and subsequent drug sequestration. Combined targeting of MEK and lysosomal function may improve sensitivity to MEK inhibition in PDAC.

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Apparent Diffusion Coefficient (ADC) predicts therapy response in pancreatic ductal adenocarcinoma

Cited by 30 publications

References 31 publications

A prospectively validated machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma

A prospectively validated machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma

MEK Inhibition Targets Cancer Stem Cells and Impedes Migration of Pancreatic Cancer CellsIn VitroandIn Vivo

TFEB-mediated lysosomal biogenesis and lysosomal drug sequestration confer resistance to MEK inhibition in pancreatic cancer

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