scholarly journals Relevance of Immune Infiltration and Clinical Outcomes in Pancreatic Ductal Adenocarcinoma Subtypes

2021 ◽  
Vol 10 ◽  
Author(s):  
Rong Liu ◽  
Ya-Zhou Liao ◽  
Wei Zhang ◽  
Hong-Hao Zhou
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Clinical Outcomes ◽  
Immune Cells ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
Critical Role ◽  
Cell Subset ◽  
Ductal Adenocarcinoma ◽  
Support Vector ◽  
Consensus Clustering

PurposePancreatic ductal adenocarcinoma (PDAC) is a lethal cancer with high heterogeneity and dismal survival rates. Tumor immune microenvironment plays a critical role in sensitive to chemotherapy and prognosis. Herein, we determined the relevance of the composition of tumor-infiltrating immune cells to clinical outcomes in PDACs, and we evaluated these effects by molecular subtype.Experimental DesignData of 1,274 samples from publically available datasets were collected. Molecular subtypes were predicted with support vector machine. Twenty-two subsets of immune cells were estimated with CIBERSORTx. The associations between each cell subset and overall survival (OS), relapse free survival (RFS), and complete response (CR) to chemotherapy were evaluated, modelling cellular proportions as quartiles.ResultsAn immune-related cluster was identified with unsupervised hierarchical clustering of hallmark pathways. Of the immune cells investigated, M0 macrophages emerged as closely associated with worse OS (HR =1.23, 95% CI = 1.15–1.31, p=1.57×10-9) and RFS (HR = 1.14, 95% CI =1.04–1.25, p=2.93×10-3), regardless of molecular subtypes. The CD8+ T cells conferred favorable survival. The neutrophils conferred poor OS overall (HR=1.17, 95% CI=1.10–1.23, p=1.74×10-7) and within the classical subtype. In the basal-like subtype, activated mast cells were associated with worse OS. Consensus clustering revealed six immune subgroups with distinct survival patterns and CR rates. The higher expression of PD1 was associated with better OS.ConclusionsThe immune cellular composition infiltrate in PDAC are likely to have effects on prognosis. Further exploration of the cellular immune response has the potential to identify candidates for immunotherapy.

scholarly journals Identification and Validation of Immune Molecular Subtypes in Pancreatic Ductal Adenocarcinoma: Implications for Prognosis and Immunotherapy

2021 ◽  
Vol 12 ◽  
Author(s):  
Ruiyu Li ◽  
Yangzhige He ◽  
Hui Zhang ◽  
Jing Wang ◽  
Xiaoding Liu ◽  
...  
Keyword(s):  
Overall Survival ◽  
Prognostic Factor ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Immune Cell ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
The Cancer Genome Atlas ◽  
Ductal Adenocarcinoma ◽  
Expression Data ◽  
Favorable Prognostic Factor

BackgroundPancreatic ductal adenocarcinoma (PDAC) remains treatment refractory. Immunotherapy has achieved success in the treatment of multiple malignancies. However, the efficacy of immunotherapy in PDAC is limited by a lack of promising biomarkers. In this research, we aimed to identify robust immune molecular subtypes of PDAC to facilitate prognosis prediction and patient selection for immunotherapy.MethodsA training cohort of 149 PDAC samples from The Cancer Genome Atlas (TCGA) with mRNA expression data was analyzed. By means of non-negative matrix factorization (NMF), we virtually dissected the immune-related signals from bulk gene expression data. Detailed immunogenomic and survival analyses of the immune molecular subtypes were conducted to determine their biological and clinical relevance. Validation was performed in five independent datasets on a total of 615 samples.ResultsApproximately 31% of PDAC samples (46/149) had higher immune cell infiltration, more active immune cytolytic activity, higher activation of the interferon pathway, a higher tumor mutational burden (TMB), and fewer copy number alterations (CNAs) than the other samples (all P < 0.001). This new molecular subtype was named Immune Class, which served as an independent favorable prognostic factor for overall survival (hazard ratio, 0.56; 95% confidence interval, 0.33-0.97). Immune Class in cooperation with previously reported tumor and stroma classifications had a cumulative effect on PDAC prognostic stratification. Moreover, programmed cell death-1 (PD-1) inhibitors showed potential efficacy for Immune Class (P = 0.04). The robustness of our immune molecular subtypes was further verified in the validation cohort.ConclusionsBy capturing immune-related signals in the PDAC tumor microenvironment, we reveal a novel molecular subtype, Immune Class. Immune Class serves as an independent favorable prognostic factor for overall survival in PDAC patients.

NIMG-46. SYSTEMATIC LITERATURE REVIEW OF ARTIFICIAL INTELLIGENCE ALGORITHMS USING PRE-THERAPY MR IMAGING FOR GLIOMA MOLECULAR SUBTYPE CLASSIFICATION

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi139-vi139
Author(s):  
Jan Lost ◽  
Tej Verma ◽  
Niklas Tillmanns ◽  
W R Brim ◽  
Harry Subramanian ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Literature Review ◽  
Prediction Accuracy ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
Tumor Resection ◽  
Support Vector ◽  
Hospital Data

Abstract PURPOSE Identifying molecular subtypes in gliomas has prognostic and therapeutic value, traditionally after invasive neurosurgical tumor resection or biopsy. Recent advances using artificial intelligence (AI) show promise in using pre-therapy imaging for predicting molecular subtype. We performed a systematic review of recent literature on AI methods used to predict molecular subtypes of gliomas. METHODS Literature review conforming to PRSIMA guidelines was performed for publications prior to February 2021 using 4 databases: Ovid Embase, Ovid MEDLINE, Cochrane trials (CENTRAL), and Web of Science core-collection. Keywords included: artificial intelligence, machine learning, deep learning, radiomics, magnetic resonance imaging, glioma, and glioblastoma. Non-machine learning and non-human studies were excluded. Screening was performed using Covidence software. Bias analysis was done using TRIPOD guidelines. RESULTS 11,727 abstracts were retrieved. After applying initial screening exclusion criteria, 1,135 full text reviews were performed, with 82 papers remaining for data extraction. 57% used retrospective single center hospital data, 31.6% used TCIA and BRATS, and 11.4% analyzed multicenter hospital data. An average of 146 patients (range 34-462 patients) were included. Algorithms predicting IDH status comprised 51.8% of studies, MGMT 18.1%, and 1p19q 6.0%. Machine learning methods were used in 71.4%, deep learning in 27.4%, and 1.2% directly compared both methods. The most common algorithm for machine learning were support vector machine (43.3%), and for deep learning convolutional neural network (68.4%). Mean prediction accuracy was 76.6%. CONCLUSION Machine learning is the predominant method for image-based prediction of glioma molecular subtypes. Major limitations include limited datasets (60.2% with under 150 patients) and thus limited generalizability of findings. We recommend using larger annotated datasets for AI network training and testing in order to create more robust AI algorithms, which will provide better prediction accuracy to real world clinical datasets and provide tools that can be translated to clinical practice.

scholarly journals The Proteomic Landscape of Pancreatic Ductal Adenocarcinoma Liver Metastases Identifies Molecular Subtypes and Associations with Clinical Response—Response

2021 ◽  
Vol 27 (14) ◽  
pp. 4127-4127
Author(s):  
Henry C.-H. Law ◽  
Emalie J. Clement ◽  
Paul M. Grandgenett ◽  
Michael A. Hollingsworth ◽  
Nicholas T. Woods
Keyword(s):  
Liver Metastases ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Clinical Response ◽  
Molecular Subtypes ◽  
Ductal Adenocarcinoma

scholarly journals The Crosstalk Between Malignant Cells and Tumor-Promoting Immune Cells Relevant to Immunotherapy in Pancreatic Ductal Adenocarcinoma

2022 ◽  
Vol 9 ◽  
Author(s):  
Xuefei Liu ◽  
Ziwei Luo ◽  
Xuechen Ren ◽  
Zhihang Chen ◽  
Xiaoqiong Bao ◽  
...  
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Rna Sequencing ◽  
Immune Cells ◽  
Immune Checkpoint ◽  
Immune Cell ◽  
Immune Checkpoint Blockade ◽  
Ductal Adenocarcinoma ◽  
Malignant Cells ◽  
Sequencing Data ◽  
Immunosuppressive Microenvironment

Background: Pancreatic ductal adenocarcinoma (PDAC) is dominated by an immunosuppressive microenvironment, which makes immune checkpoint blockade (ICB) often non-responsive. Understanding the mechanisms by which PDAC forms an immunosuppressive microenvironment is important for the development of new effective immunotherapy strategies.Methods: This study comprehensively evaluated the cell-cell communications between malignant cells and immune cells by integrative analyses of single-cell RNA sequencing data and bulk RNA sequencing data of PDAC. A Malignant-Immune cell crosstalk (MIT) score was constructed to predict survival and therapy response in PDAC patients. Immunological characteristics, enriched pathways, and mutations were evaluated in high- and low MIT groups.Results: We found that PDAC had high level of immune cell infiltrations, mainly were tumor-promoting immune cells. Frequent communication between malignant cells and tumor-promoting immune cells were observed. 15 ligand-receptor pairs between malignant cells and tumor-promoting immune cells were identified. We selected genes highly expressed on malignant cells to construct a Malignant-Immune Crosstalk (MIT) score. MIT score was positively correlated with tumor-promoting immune infiltrations. PDAC patients with high MIT score usually had a worse response to immune checkpoint blockade (ICB) immunotherapy.Conclusion: The ligand-receptor pairs identified in this study may provide potential targets for the development of new immunotherapy strategy. MIT score was established to measure tumor-promoting immunocyte infiltration. It can serve as a prognostic indicator for long-term survival of PDAC, and a predictor to ICB immunotherapy response.

scholarly journals Infiltrating immune cells and gene mutations in pancreatic ductal adenocarcinoma

2016 ◽  
Vol 103 (9) ◽  
pp. 1189-1199 ◽  
Author(s):  
W.-Q. Wang ◽  
L. Liu ◽  
H.-X. Xu ◽  
C.-T. Wu ◽  
J.-F. Xiang ◽  
...  
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Immune Cells ◽  
Gene Mutations ◽  
Ductal Adenocarcinoma

Morphological classification of pancreatic ductal adenocarcinoma that predicts molecular subtypes and correlates with clinical outcome

Gut ◽  
2019 ◽  
Vol 69 (2) ◽  
pp. 317-328 ◽  
Author(s):  
Sangeetha N Kalimuthu ◽  
Gavin W Wilson ◽  
Robert C Grant ◽  
Matthew Seto ◽  
Grainne O’Kane ◽  
...  
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Molecular Subtypes ◽  
Differentially Expressed Gene ◽  
Biological Significance ◽  
Treatment Strategies ◽  
Ductal Adenocarcinoma ◽  
Rna Seq ◽  
Gene Signatures ◽  
Significant Difference ◽  
Morphological Patterns

IntroductionTranscriptional analyses have identified several distinct molecular subtypes in pancreatic ductal adenocarcinoma (PDAC) that have prognostic and potential therapeutic significance. However, to date, an indepth, clinicomorphological correlation of these molecular subtypes has not been performed. We sought to identify specific morphological patterns to compare with known molecular subtypes, interrogate their biological significance, and furthermore reappraise the current grading system in PDAC.DesignWe first assessed 86 primary, chemotherapy-naive PDAC resection specimens with matched RNA-Seq data for specific, reproducible morphological patterns. Differential expression was applied to the gene expression data using the morphological features. We next compared the differentially expressed gene signatures with previously published molecular subtypes. Overall survival (OS) was correlated with the morphological and molecular subtypes.ResultsWe identified four morphological patterns that segregated into two components (‘gland forming’ and ‘non-gland forming’) based on the presence/absence of well-formed glands. A morphological cut-off (≥40% ‘non-gland forming’) was established using RNA-Seq data, which identified two groups (A and B) with gene signatures that correlated with known molecular subtypes. There was a significant difference in OS between the groups. The morphological groups remained significantly prognostic within cancers that were moderately differentiated and classified as ‘classical’ using RNA-Seq.ConclusionOur study has demonstrated that PDACs can be morphologically classified into distinct and biologically relevant categories which predict known molecular subtypes. These results provide the basis for an improved taxonomy of PDAC, which may lend itself to future treatment strategies and the development of deep learning models.

Breast cancer molecular subtypes association with clinical outcomes and race.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e12575-e12575 ◽  
Author(s):  
Ramses F. Sadek ◽  
Li fang Zhang ◽  
Houssein Talal Abdul Sater
Keyword(s):  
Breast Cancer ◽  
Black Women ◽  
Clinical Outcomes ◽  
Cox Regression ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
Cancer Stage ◽  
Luminal A ◽  
Luminal B ◽  
Race Disparity

e12575 Background: Breast Cancer (BC) has been classified into four subtypes: Luminal A (LABC), Luminal B (LBBC), Triple negative (TNBC) and HER2-enriched (HER2e). BC mortality in Black women is significantly higher than in Whites and Asians. BC in Blacks has been characterized by higher grade and later stage. Causes of the Black-White BC survival disparity have been investigated, including differences in: diagnostic stage, socioeconomics, and comorbidities. These have led researchers to investigate the differences in tumor molecular subtype and their association with clinical outcomes and races. Methods: This study used the Surveillance, Epidemiology, and End Results – 18 (SEER-18) Registries research data between 2010 and 2013 that included over 212,000 patients. Descriptive statistics, Odds ratios (OR) and 95%Confidence intervals (CI) were used to study the association between BC stage, grade, and mortality and BC molecular subtypes across different races. We employed Cox regression models to explore the race disparity in BC mortality before and after controlling for BC molecular subtype and other clinical and social factors. Results: TNBC had more high grade cancer compared to HER2e subtype (OR, 1.5; CI, 1.3 - 1.8), LBBC (OR, 4.5; CI, 4.0 - 5.0) and LABC (OR, 12.2; CI, 11.2 – 13.3) for Black. BC mortality was higher in TNBC subtype compared to HER2e subtype (OR, 1.3; CI, 1.1 - 1.6), LBBC (OR, 2.4; CI, 2.0 - 2.9), and LABC (OR, 2.8; CI, 2.4 – 3.2) for Blacks. Results are consistent for all races. HER2e subtype had more late cancer stage compare to LBBC (OR, 1.2; CI, 1.0 - 1.4), TNBC (OR, 1.4; CI, 1.2 - 1.6) and LABC (OR, 2.1; CI, 1.8 - 2.4) in Blacks with similar results in all races. BC mortality in Blacks was higher compare with Whites (HR, 1.9; CI, 1.8 - 2.0) and Asian (HR, 2.7; CI, 2.5 - 3.0). After controlling for cancer subtype and other factors in the Cox regression model, the corresponding HRs ware significantly decreased to 1.2 (CI, 1.1 -1.3) and 1.6 (9CI, 1.5 -1.8). Blacks have heighst percent in stage IV and grade higer grade of disease. Conclusions: Molecular subtypes of BC contribute differently to risks of late cancer stage, high cancer grade and BC specific mortality. These differences are consistent in all races. The molecular subtypes and other social and clinical factors may explain part of the BC mortality race disparity.

Significant neutrophil accumulation, IL-18 deposition, and active inflammasome in tumor regions of human pancreatic ductal adenocarcinoma.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e15754-e15754
Author(s):  
Ahmed Khattab ◽  
Yesica Garciafigueroa ◽  
Brett Phillips ◽  
Sunita Patruni ◽  
Amir Kamran ◽  
...  
Keyword(s):  
Risk Factor ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Immune Cells ◽  
Significant Risk ◽  
Innate Immune ◽  
Ductal Adenocarcinoma ◽  
Innate Immune Cells ◽  
Neutrophil Accumulation ◽  
Alexa Fluor ◽  
Tumor Environment

e15754 Background: Obesity is an established risk factor for cancer and cancer-related mortality. Adipocytes may act as a “damage” signal resulting in the accumulation of innate immune cells, fueling an inflammatory micro-environment. Among the innate immune cells, tumor-associated neutrophils (TANs) are confirmed to accumulate inside the tumor. As obesity is a significant risk factor for a poorer prognosis in pancreatic ductal adenocarcinoma (PDAC), we hypothesize that obesity-driven adipose fuels the progression of PDAC in a TAN and inflammasome-facilitated manner. This may happen through several mechanisms, including genomic instability of aggressive PDAC clonal populations. Methods: Tissue from resected tumors of PDAC patients with a body-mass index (BMI) > 27 (obesity, n = 5) and BMI < 22 (normal, n = 5) were incubated with anti-human IL-18 and CD66b antibodies and detected using Alexa Fluor 488-conjugated donkey anti–mouse and Alexa Fluor 594-conjugated goat anti-rabbit secondary antibodies. Images were acquired in a Zeiss Axioplan microscope workstation and analyzed by ZEN and Metamorph software. Results: We identified the presence of CD66b+ neutrophils and IL-18 in all tissue sections. There is a significantly greater density of CD66b+ neutrophils and IL-18 in the tumor environment when compared to adjacent normal areas and IL-18+ signals appear to be deposited alongside the luminal area in a unique lasso-like pattern. These data suggest an association between BMI and inflammasome accumulation in the tumor environment. There does not appear to be an association between tumor stage and TAN or IL-18 accumulation in the tumor area or the adjacent normal tissue in the tissues. Conclusions: Our data uniquely shows that CD66b+ neutrophils accumulate inside PDAC and this is associated with intra-tumoral active inflammasomes. The novel periductal deposition of IL-18 in the tumor areas may be of mechanistic relevance in understanding how inflammation induces progression of PDAC. A larger cohort of tissues from PDAC obtained from low and high BMI individuals will be needed to test our hypothesis and to begin to decipher the role of obesity on inflammation and PDAC progression.

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