Stroma and Immune cell infiltration are increased in pancreatic carcinoma after neoadjuvant therapy

Pancreatology ◽  
2018 ◽  
Vol 18 (4) ◽  
pp. S56-S57
Author(s):  
Li Xie ◽  
Leizhou Xia ◽  
Ulla Klaiber ◽  
Milena Sachsenmaier ◽  
Oliver Strobel ◽  
...  
2021 ◽  
Vol 11 ◽  
Author(s):  
De Luo ◽  
Fei Kuang ◽  
Juan Du ◽  
Mengjia Zhou ◽  
Fangyi Peng ◽  
...  

The tumor microenvironment (TME) is comprised of tumor cells, infiltrating immune cells, and stroma. Multiple reports suggest that the immune cell infiltration (ICI) in TME is strongly associated with responsiveness to immunotherapy and prognosis of certain cancers. Thus far, the ICI profile of pancreatic carcinoma (PC) remains unclear. Here, we employed two algorithms to characterize the ICI profile of PC patients. Based on our results, we identified 2 ICI patterns and calculated the ICI score by using principal component analysis. Furthermore, we revealed that patients with low ICI scores had a better prognosis, compared to high ICI scores. Moreover, we discovered that a low tumor mutation burden (TMB) offered better overall survival (OS), relative to high TMB. In this study, a high ICI score referred to elevated PD-L1/TGF-β levels, increased activation of cell cycle pathway and DNA repair pathway, as well as reduced expression of immune-activation-related genes. We also demonstrated that three metabolic pathways were suppressed in the low ICI score group. These data may explain why a high ICI score equates to a poor prognosis. Based on our analysis, the ICI score can be used as an effective predictor of PC prognosis. Hence, establishing an ICI profile, based on a large patient population, will not only enhance our knowledge of TME but also aid in the development of immunotherapies specific to PC.


2015 ◽  
Vol 53 (12) ◽  
Author(s):  
AB Widera ◽  
L Pütter ◽  
S Leserer ◽  
G Campos ◽  
K Rochlitz ◽  
...  

Author(s):  
Lu Yuan ◽  
Xixi Wu ◽  
Longshan Zhang ◽  
Mi Yang ◽  
Xiaoqing Wang ◽  
...  

AbstractPulmonary surfactant protein A1 (SFTPA1) is a member of the C-type lectin subfamily that plays a critical role in maintaining lung tissue homeostasis and the innate immune response. SFTPA1 disruption can cause several acute or chronic lung diseases, including lung cancer. However, little research has been performed to associate SFTPA1 with immune cell infiltration and the response to immunotherapy in lung cancer. The findings of our study describe the SFTPA1 expression profile in multiple databases and was validated in BALB/c mice, human tumor tissues, and paired normal tissues using an immunohistochemistry assay. High SFTPA1 mRNA expression was associated with a favorable prognosis through a survival analysis in lung adenocarcinoma (LUAD) samples from TCGA. Further GeneOntology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses showed that SFTPA1 was involved in the toll-like receptor signaling pathway. An immune infiltration analysis clarified that high SFTPA1 expression was associated with an increased number of M1 macrophages, CD8+ T cells, memory activated CD4+ T cells, regulatory T cells, as well as a reduced number of M2 macrophages. Our clinical data suggest that SFTPA1 may serve as a biomarker for predicting a favorable response to immunotherapy for patients with LUAD. Collectively, our study extends the expression profile and potential regulatory pathways of SFTPA1 and may provide a potential biomarker for establishing novel preventive and therapeutic strategies for lung adenocarcinoma.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alexander J. Dwyer ◽  
Jacob M. Ritz ◽  
Jason S. Mitchell ◽  
Tijana Martinov ◽  
Mohannad Alkhatib ◽  
...  

AbstractThe notion that T cell insulitis increases as type 1 diabetes (T1D) develops is unsurprising, however, the quantitative analysis of CD4+ and CD8+ T cells within the islet mass is complex and limited with standard approaches. Optical microscopy is an important and widely used method to evaluate immune cell infiltration into pancreatic islets of Langerhans for the study of disease progression or therapeutic efficacy in murine T1D. However, the accuracy of this approach is often limited by subjective and potentially biased qualitative assessment of immune cell subsets. In addition, attempts at quantitative measurements require significant time for manual analysis and often involve sophisticated and expensive imaging software. In this study, we developed and illustrate here a streamlined analytical strategy for the rapid, automated and unbiased investigation of islet area and immune cell infiltration within (insulitis) and around (peri-insulitis) pancreatic islets. To this end, we demonstrate swift and accurate detection of islet borders by modeling cross-sectional islet areas with convex polygons (convex hulls) surrounding islet-associated insulin-producing β cell and glucagon-producing α cell fluorescent signals. To accomplish this, we used a macro produced with the freeware software ImageJ equipped with the Fiji Is Just ImageJ (FIJI) image processing package. Our image analysis procedure allows for direct quantification and statistical determination of islet area and infiltration in a reproducible manner, with location-specific data that more accurately reflect islet areas as insulitis proceeds throughout T1D. Using this approach, we quantified the islet area infiltrated with CD4+ and CD8+ T cells allowing statistical comparison between different age groups of non-obese diabetic (NOD) mice progressing towards T1D. We found significantly more CD4+ and CD8+ T cells infiltrating the convex hull-defined islet mass of 13-week-old non-diabetic and 17-week-old diabetic NOD mice compared to 4-week-old NOD mice. We also determined a significant and measurable loss of islet mass in mice that developed T1D. This approach will be helpful for the location-dependent quantitative calculation of islet mass and cellular infiltration during T1D pathogenesis and can be combined with other markers of inflammation or activation in future studies.


Bioengineered ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 3410-3425
Author(s):  
Xiangzhou Tan ◽  
Linfeng Mao ◽  
Changhao Huang ◽  
Weimin Yang ◽  
Jianping Guo ◽  
...  

2021 ◽  
Vol 94 ◽  
pp. 107446
Author(s):  
Kuipeng Yu ◽  
Dengren Li ◽  
Fuping Xu ◽  
Hao Guo ◽  
Feng Feng ◽  
...  

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