Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features

PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.     

Immune Checkpoint Inhibitor Toxicities

Immune checkpoint inhibitors are molecules that increase the endogenous immune response against tumors. They have revolutionized the field of oncology. Since their initial approval for the treatment of advanced melanoma, their use has expanded to the treatment of several other advanced cancers. Unfortunately, immune checkpoint inhibitors have also been associated with the emergence of a new subset of autoimmune-like toxicities, known as immune-related adverse events. These toxicities differ depending on the agent, malignancy, and individual susceptibilities. Although the skin and colon are most commonly involved, any organ may be affected, including the liver, lungs, kidneys, and heart. Most of these toxicities are diagnosed by excluding other secondary infectious or inflammatory causes. Corticosteroids are commonly used for treatment of moderate and severe immune-related adverse events, although additional immunosuppressive therapy may occasionally be required. The occurrence of immune-related toxicities may require discontinuation of immunotherapy, depending on the specific toxicity and its severity. In this article, we provide a focused review to familiarize practicing clinicians with this important topic given that the use of immune checkpoint inhibitors continues to increase.     

Combining CAPRA-S With Tumor IDC/C Features Improves the Prognostication of Biochemical Recurrence in Prostate Cancer Patients

Background: Intraductal carcinoma and cribriform (IDC/C) tumor features are well-established prognosticators of biochemical recurrence (BCR), metastasis, and prostate cancer (PCa)-specific mortality. However, approximately 70% of PCa patients undergoing a radical prostatectomy are IDC/C negative, yet up-to 20% of these patients progress and experience BCR. Thus, tumor histopathologic characteristics such as IDC/C alone are limited in their ability to predict disease progression. Conversely, several nomograms such as Cancer of the Prostate Risk Assessment-Surgery (CAPRA-S) have been developed to aid in the prognostication of BCR, but not yet widely applied in clinical settings. Materials and methods: In this study, we assessed the combined prognostic utility of IDC/C, and CAPRA-S for BCR in 3 PCa patient cohorts. Results: CAPRA-S+IDC/C improved the predictive accuracy of BCR in all 3 cohorts (P < .001). Specifically, among IDC/C negative cases, CAPRA-S improved the prognostication of BCR in low-risk (Cohort 1; P < .001, Cohort 2; P < .001, Cohort 3; P = .003), intermediate (Cohort 1; P < .001, Cohort 2; P = .006, Cohort 3; P = .03) and high-risk (Cohort 1-3; P < .001) patients. Conversely, IDC/C improved the prognostication of BCR among CAPRA-S low-risk (Cohorts 1; P < .001 and Cohort 3; P = .003) patients. Conclusion: Our results suggest the investigation of histopathological IDC/C features in CAPRA-S low-risk patients and conversely, nomogram CAPRA-S among IDC/C negative patients improves the identification of patients likely to experience BCR, which would otherwise be missed through current assessment regimens. These patients can be offered more intensive monitoring and adjuvant therapies upfront to circumvent the development of recurrent cancer or overtreatment at the time of surgery.     

A DNA vaccine expressing an optimized secreted FAPα induces enhanced anti-tumor activity by altering the tumor microenvironment in a murine model of breast cancer

Cancer-associated fibroblasts (CAFs), major components of the tumor microenvironment (TME), promote tumor growth and metastasis and inhibit the anti-tumor immune response. We previously constructed a DNA vaccine expressing human FAPα, which is highly expressed by CAFs, to target these cells in the TME, and observed limited anti-tumor effects in the 4T1 breast cancer model. When the treatment time was delayed until tumor nodes formed, the anti-tumor effect of the vaccine completely disappeared. In this study, to improve the safety and efficacy, we constructed a new FAPα-targeted vaccine containing only the extracellular domain of human FAPα with a tissue plasminogen activator signal sequence for enhanced antigen secretion and immunogenicity. The number of CAFs was more effectively reduced by CD8⁺ T cells induced by the new vaccine. This resulted in decreases in CCL2 and CXCL12 expression, leading to a significant decrease in the ratio of myeloid-derived suppressor cells in the TME. Moreover, when mice were treated after the establishment of tumors, the vaccine could still delay tumor growth. To facilitate the future application of the vaccine in clinical trials, we further optimized the gene codons and reduced the homology between the vaccine and the original sequence, which may be convenient for evaluating the vaccine distribution in the human body. These results indicated that the new FAPα-targeted vaccine expressing an optimized secreted human FAPα induced enhanced anti-tumor activity by reducing the number of FAPα⁺ CAFs and enhancing the recruitment of effector T cells in the 4T1 tumor model mice.     

Melatonin induces apoptosis of colorectal cancer cells through HDAC4 nuclear import mediated by CaMKII inactivation

Melatonin induces apoptosis in many different cancer cell lines, including colorectal cancer. However, the precise mechanisms involved remain largely unresolved. In this study, we provide evidence to reveal a new mechanism by which melatonin induces apoptosis of colorectal cancer LoVo cells. Melatonin at pharmacological concentrations significantly suppressed cell proliferation and induced apoptosis in a dose‐dependent manner. The observed apoptosis was accompanied by the melatonin‐induced dephosphorylation and nuclear import of histone deacetylase 4 (HDAC4). Pretreatment with a HDAC4‐specific siRNA effectively attenuated the melatonin‐induced apoptosis, indicating that nuclear localization of HDAC4 is required for melatonin‐induced apoptosis. Moreover, constitutively active Ca²⁺/calmodulin‐dependent protein kinase II alpha (CaMKIIα) abrogated the melatonin‐induced HDAC4 nuclear import and apoptosis of LoVo cells. Furthermore, melatonin decreased H3 acetylation on bcl‐2 promoter, leading to a reduction of bcl‐2 expression, whereas constitutively active CaMKIIα(T286D) or HDAC4‐specific siRNA abrogated the effect of melatonin. In conclusion, the present study provides evidence that melatonin‐induced apoptosis in colorectal cancer LoVo cells largely depends on the nuclear import of HDAC4 and subsequent H3 deacetylation via the inactivation of CaMKIIα.     

In Vitro-In Vivo Extrapolation and Hepatic Clearance-Dependent Underprediction

Accurately predicting the hepatic clearance of compounds using in vitro to in vivo extrapolation (IVIVE) is crucial within the pharmaceutical industry. However, several groups have recently highlighted the serious error in the process. Although empirical or regression-based scaling factors may be used to mitigate the common underprediction, they provide unsatisfying solutions because the reasoning behind the underlying error has yet to be determined. One previously noted trend was intrinsic clearance-dependent underprediction, highlighting the limitations of current in vitro systems. When applying these generated in vitro intrinsic clearance values during drug development and making first-in-human dose predictions for new chemical entities though, hepatic clearance is the parameter that must be estimated using a model of hepatic disposition, such as the well-stirred model. Here, we examine error across hepatic clearance ranges and find a similar hepatic clearance-dependent trend, with high clearance compounds not predicted to be so, demonstrating another gap in the field.