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Background: The receptor tyrosine kinases TAM family (TYRO3, AXL, and MERTK) are highly expressed in multiple forms of cancer cells and tumor-associated macrophages and promote the development of cancers including pancreatic tumor. Targeting TAM receptors could be a promising therapeutic option. Methods: We designed a novel CAR based on the extracellular domain of growth arrest-specific protein 6 (GAS6), a natural ligand for all TAM members. The ability of CAR-T to kill pancreatic cancer cells is tested in vitro and in vivo, and the safety is evaluated in mice and nonhuman primate. Results: GAS6-CAR-T cells efficiently kill TAM-positive pancreatic cancer cell lines, gemcitabine-resistant cancer cells, and cancer stem-like cells in vitro. GAS6-CAR-T cells also significantly suppressed the growth of PANC1 xenografts and patient-derived xenografts in mice. Furthermore, these CAR-T cells did not induce obvious side effects in nonhuman primate or mice although the CAR was demonstrated to recognize mouse TAM. Conclusions: Our findings indicate that GAS6-CAR-T-cell therapy may be effective for pancreatic cancers with low toxicity. [ABSTRACT FROM AUTHOR]

Background: CT is the major detection tool for pancreatic cancer (PC). However, approximately 40% of PCs < 2 cm are missed on CT, underscoring a pressing need for tools to supplement radiologist interpretation. Methods: Contrast-enhanced CT studies of 546 patients with pancreatic adenocarcinoma diagnosed by histology/cytology between January 2005 and December 2019 and 733 CT studies of controls with normal pancreas obtained between the same period in a tertiary referral center were retrospectively collected for developing an automatic end-to-end computer-aided detection (CAD) tool for PC using two-dimensional (2D) and three-dimensional (3D) radiomic analysis with machine learning. The CAD tool was tested in a nationwide dataset comprising 1,477 CT studies (671 PCs, 806 controls) obtained from institutions throughout Taiwan. Results: The CAD tool achieved 0.918 (95% CI, 0.895–0.938) sensitivity and 0.822 (95% CI, 0.794–0.848) specificity in differentiating between studies with and without PC (area under curve 0.947, 95% CI, 0.936–0.958), with 0.707 (95% CI, 0.602–0.797) sensitivity for tumors < 2 cm. The positive and negative likelihood ratios of PC were 5.17 (95% CI, 4.45–6.01) and 0.10 (95% CI, 0.08–0.13), respectively. Where high specificity is needed, using 2D and 3D analyses in series yielded 0.952 (95% CI, 0.934–0.965) specificity with a sensitivity of 0.742 (95% CI, 0.707–0.775), whereas using 2D and 3D analyses in parallel to maximize sensitivity yielded 0.915 (95% CI, 0.891–0.935) sensitivity at a specificity of 0.791 (95% CI, 0.762–0.819). Conclusions: The high accuracy and robustness of the CAD tool supported its potential for enhancing the detection of PC. [ABSTRACT FROM AUTHOR]

Background: CT is the major detection tool for pancreatic cancer (PC). However, approximately 40% of PCs < 2 cm are missed on CT, underscoring a pressing need for tools to supplement radiologist interpretation. Methods: Contrast-enhanced CT studies of 546 patients with pancreatic adenocarcinoma diagnosed by histology/cytology between January 2005 and December 2019 and 733 CT studies of controls with normal pancreas obtained between the same period in a tertiary referral center were retrospectively collected for developing an automatic end-to-end computer-aided detection (CAD) tool for PC using two-dimensional (2D) and three-dimensional (3D) radiomic analysis with machine learning. The CAD tool was tested in a nationwide dataset comprising 1,477 CT studies (671 PCs, 806 controls) obtained from institutions throughout Taiwan. Results: The CAD tool achieved 0.918 (95% CI, 0.895–0.938) sensitivity and 0.822 (95% CI, 0.794–0.848) specificity in differentiating between studies with and without PC (area under curve 0.947, 95% CI, 0.936–0.958), with 0.707 (95% CI, 0.602–0.797) sensitivity for tumors < 2 cm. The positive and negative likelihood ratios of PC were 5.17 (95% CI, 4.45–6.01) and 0.10 (95% CI, 0.08–0.13), respectively. Where high specificity is needed, using 2D and 3D analyses in series yielded 0.952 (95% CI, 0.934–0.965) specificity with a sensitivity of 0.742 (95% CI, 0.707–0.775), whereas using 2D and 3D analyses in parallel to maximize sensitivity yielded 0.915 (95% CI, 0.891–0.935) sensitivity at a specificity of 0.791 (95% CI, 0.762–0.819). Conclusions: The high accuracy and robustness of the CAD tool supported its potential for enhancing the detection of PC. [ABSTRACT FROM AUTHOR]