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Localization of KRAS downstream target ARL4C to invasive pseudopods accelerates pancreatic cancer cell invasion.

ABSTRACT: Pancreatic cancer has a high mortality rate due to metastasis. Whereas KRAS is mutated in most pancreatic cancer patients, controlling KRAS or its downstream effectors has not been succeeded clinically. ARL4C is a small G protein whose expression is induced by the Wnt and EGF-RAS pathways. In the present study, we found that ARL4C is frequently overexpressed in pancreatic cancer patients and showed that its localization to invasive pseudopods is required for cancer cell invasion. IQGAP1 was identified as a novel interacting protein for ARL4C. ARL4C recruited IQGAP1 and its downstream effector, MMP14, to invasive pseudopods. Specific localization of ARL4C, IQGAP1, and MMP14 was the active site of invasion, which induced degradation of the extracellular matrix. Moreover, subcutaneously injected antisense oligonucleotide against ARL4C into tumor-bearing mice suppressed metastasis of pancreatic cancer. These results suggest that ARL4C-IQGAP1-MMP14 signaling is activated at invasive pseudopods of pancreatic cancer cells.

SUBMITTER: Harada A

PROVIDER: S-EPMC8598236 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Localization of KRAS downstream target ARL4C to invasive pseudopods accelerates pancreatic cancer cell invasion.

Harada Akikazu A Matsumoto Shinji S Yasumizu Yoshiaki Y Shojima Kensaku K Akama Toshiyuki T Eguchi Hidetoshi H Kikuchi Akira A

eLife 20210930

Pancreatic cancer has a high mortality rate due to metastasis. Whereas KRAS is mutated in most pancreatic cancer patients, controlling KRAS or its downstream effectors has not been succeeded clinically. ARL4C is a small G protein whose expression is induced by the Wnt and EGF-RAS pathways. In the present study, we found that ARL4C is frequently overexpressed in pancreatic cancer patients and showed that its localization to invasive pseudopods is required for cancer cell invasion. IQGAP1 was iden ...[more]

PMID: 34590580

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Project description:PurposeDeep learning is an emerging technique that allows us to capture imaging information beyond the visually recognizable level of a human being. Because of the anatomic characteristics and location, on-board target verification for radiation delivery to pancreatic tumors is a challenging task. Our goal was to use a deep neural network to localize the pancreatic tumor target on kV x-ray images acquired using an on-board imager for image guided radiation therapy.Methods and materialsThe network is set up in such a way that the input is either a digitally reconstructed radiograph image or a monoscopic x-ray projection image acquired by the on-board imager from a given direction, and the output is the location of the planning target volume in the projection image. To produce a sufficient number of training x-ray images reflecting the vast number of possible clinical scenarios of anatomy distribution, a series of changes were introduced to the planning computed tomography images, including deformation, rotation, and translation, to simulate inter- and intrafractional variations. After model training, the accuracy of the model was evaluated by retrospectively studying patients who underwent pancreatic cancer radiation therapy. Statistical analysis using mean absolute differences (MADs) and Lin's concordance correlation coefficient were used to assess the accuracy of the predicted target positions.ResultsMADs between the model-predicted and the actual positions were found to be less than 2.60 mm in anteroposterior, lateral, and oblique directions for both axes in the detector plane. For comparison studies with and without fiducials, MADs are less than 2.49 mm. For all cases, Lin's concordance correlation coefficients between the predicted and actual positions were found to be better than 93%, demonstrating the success of the proposed deep learning for image guided radiation therapy.ConclusionsWe demonstrated that markerless pancreatic tumor target localization is achievable with high accuracy by using a deep learning technique approach.

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Localization of KRAS downstream target ARL4C to invasive pseudopods accelerates pancreatic cancer cell invasion.

Publications

Localization of KRAS downstream target ARL4C to invasive pseudopods accelerates pancreatic cancer cell invasion.

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