scholarly journals Integrative genomic analyses of APOBEC-mutational signature, expression and germline deletion of APOBEC3 genes, and immunogenicity in multiple cancer types

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Zhishan Chen ◽  
Wanqing Wen ◽  
Jiandong Bao ◽  
Krystle L. Kuhs ◽  
Qiuyin Cai ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Immune Cell ◽  
Expression Level ◽  
Multiple Cancer ◽  
Association Analyses ◽  
Mutational Signature ◽  
Immunological Mechanisms ◽  
Cancer Types ◽  
Using Data ◽  
Relationship Of

Abstract Background Although APOBEC-mutational signature is found in tumor tissues of multiple cancers, how a common germline APOBEC3A/B deletion affects the mutational signature remains unclear. Methods Using data from 10 cancer types generated as part of TCGA, we performed integrative genomic and association analyses to assess inter-relationship of expressions for isoforms APOBEC3A and APOBEC3B, APOBEC-mutational signature, germline APOBEC3A/B deletions, neoantigen loads, and tumor infiltration lymphocytes (TILs). Results We found that expression level of the isoform uc011aoc transcribed from the APOBEC3A/B chimera was associated with a greater burden of APOBEC-mutational signature only in breast cancer, while germline APOBEC3A/B deletion led to an increased expression level of uc011aoc in multiple cancer types. Furthermore, we found that the deletion was associated with elevated APOBEC-mutational signature, neoantigen loads and relative composition of T cells (CD8+) in TILs only in breast cancer. Additionally, we also found that APOBEC-mutational signature significantly contributed to neoantigen loads and certain immune cell abundances in TILs across cancer types. Conclusions These findings reveal new insights into understanding the genetic, biological and immunological mechanisms through which APOBEC genes may be involved in carcinogenesis, and provide potential genetic biomarker for the development of disease prevention and cancer immunotherapy.

Novel association of pre-chemotherapy immune cell profiles with functional decline and resilience in women with breast cancer receiving chemotherapy.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 549-549
Author(s):  
Nikesha Gilmore ◽  
Supriya Gupta Mohile ◽  
Huiwen Xu ◽  
Kah Poh Loh ◽  
Amber Kleckner ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Physical Function ◽  
Immune Cell ◽  
Functional Decline ◽  
Cell Counts ◽  
Increased Risk ◽  
Novel Association ◽  
Pre Treatment ◽  
General Physical ◽  
Relationship Of

549 Background: Chemotherapy adversely affects physical function. While many patients recover after treatment (i.e. are resilient), some are unable to return to their pre-treatment function (i.e. are non-resilient). Since immune dysfunction may play a role in functional decline, we assessed the relationship of pre-chemotherapy immune cell profiles with functional decline and resilience in women with breast cancer receiving chemotherapy. Methods: This study was based on a large nationwide cohort study in women with stage I-III breast cancer. Physical function was measured by the Functional Assessment of Cancer Therapy: General – Physical subscale (FACT-PWB) ≤7 days before chemotherapy (T1), ≤1 month after chemotherapy (T2), and 6 months after T2 (T3). Functional decline at T2 and T3 was defined as > 1 point decrease (clinically meaningful difference) in FACT-PWD score from T1. Patients were considered non-resilient if they had T2 functional decline and did not return to within 1 point of their baseline FACT-PWB score by T3. Immune cell counts, neutrophil:lymphocyte ratio (NLR), and lymphocyte:monocyte ratio (LMR) were obtained at T1. Multivariate logistic regressions were used to determine whether immune cell counts and ratios were associated with functional decline and being non-resilient controlling for baseline FACT-PWD, age, race, education, and marital status. Results: One-third of patients (178/529; mean age 53, range 22-81) had functional decline from T1-T3. Of the 59% (n = 310) of patients with functional decline at T2, 50% (n = 147) did not recover by T3 (i.e. were non-resilient). Patients with a low ( < median) NLR at T1 were twice as likely to have functional decline by T3 than those with a high (≥ median) NLR [Adjusted Odds Ratio (AOR) 1.8, 95% CI: 1.2-2.8, p < 0.01]. Similarly, in patients with functional decline at T2, those with a low NLR at T1 were twice as likely to be non-resilient than those with high NLR (AOR: 1.9, 95% CI: 1.1-3.2, p = 0.01). Conversely, patients with high T1 lymphocytes were twice as likely to be non-resilient than those with low lymphocytes (AOR: 1.8, 95% CI: 1.1-3.1, p = 0.02). Conclusions: One-third of women with breast cancer have clinically meaningful, persistent functional decline six months after completing chemotherapy. Higher pre-chemotherapy lymphocytes and lower NLR may be useful to identify which women are at increased risk of functional decline and reduced ability to regain baseline physical function. These findings can inform interventions to ameliorate this decline.

Identification of kinase domain duplications across diverse cancer types in Chinese population by comprehensive genomic profiling (CGP).

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e13522-e13522
Author(s):  
Baohua Wang ◽  
Ruoying Yu ◽  
Qiuxiang Ou ◽  
Hua Bao ◽  
Xue Wu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gastric Cancer ◽  
Lung Cancer ◽  
Total Population ◽  
Target Therapy ◽  
Kinase Domain ◽  
Genomic Profiling ◽  
Multiple Cancer ◽  
Comprehensive Genomic Profiling ◽  
Cancer Types

e13522 Background: Kinase domain duplication (KDD) has recently been recognized as oncogenic and targetable mutations in some cancers. EGFR KDD was identified as an oncogenic driver in lung cancer showing partial response to EGFR TKI. BRAF KDD was reported in diverse tumor types with clinical response to RAF-targeted therapy. We retrospectively investigated the prevalence of KDDs in multiple cancer types of a large Chinese population. Methods: 50742 unique cancer cases were analyzed by comprehensive genomic profiling (CGP). DNA was extracted from formalin-fixed paraffin-embedded specimens (FFPEs), fresh tissue, blood or plasma samples and sequenced with gene panels targeting 400+ cancer-relevant genes. Among them, 53 cases were detected with KDD of various kinases. Results: In this Chinese cohort, KDD was identified in 0.1% of the total population across multiple cancer types including lung cancer (39), breast cancer (5), gastric cancer (3), colorectal cancer (2), mucoepidermoid carcinoma (1), unknown (3). The median age at diagnosis was 54 which was younger than the 60 yrs median age of total population. The distribution of KDDs was in EGFR(34) MET(5), JAK1(2), BRAF(2), FGFR2(2), FGFR1(1), JAK2(1), LYN(1), MAP3K1(1), RAF1(1), RET(1), AKT3(1), and CDK8(1). Thirty-one lung cancer cases were detected with EGFR-KDD, including kinase duplications of exon18-25 (22), exon17-25 (6), exon18-26 (2), exon14-26(1). Three patients with EGFR-KDD exon18-25 showed partial anti-tumor response to target therapy. MET-KDD was exclusively found in lung cancer involving the duplication of MET exon15-21 (2), exon14-17 (1), exon15-16 (1) and exon12-21 (1) while FGFR1/2-KDD was observed only in gastric cancer. Two female patients with breast cancer were detected with JAK1-KDD at age of 45 and 37, respectively. The canonical BRAF-KDD of exon 10-18 was identified in one female patient diagnosed of lung adenocarcinoma at age of 49. Frequently altered genes in patients with KDD were TP53(72%), EGFR (23%), FAT1(13%), BRCA1(10%). MCL1 amplification, a known oncogenic alteration, was identified in fifteen patients (11 EGFR-KDD,2 MET-KDD, 1 BRAF-KDD, 1 JAK2-KDD), representing the most common copy number variation observed. Conclusions: Kinase KDDs were rare but potentially oncogenic mutations in diverse cancer types with clinical outcome of EGFR-KDD to target therapy in lung cancer. Cancer-type specific KDDs were identified including MET-KDD in lung cancer, FGFR1/2-KDD in gastric cancer and JAK1-KDD in breast cancer.

scholarly journals High dimensional immunotyping of the obese tumor microenvironment reveals model specific adaptation

2020 ◽  
Author(s):  
Cara E Wogsland ◽  
Hilde E Lien ◽  
Line Pedersen ◽  
Pahul Hanjra ◽  
Sturla M Grondal ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Immune Cell ◽  
Cell Types ◽  
High Dimensional ◽  
Low Grade ◽  
Cancer Model ◽  
Mass Cytometry ◽  
Batch Correction ◽  
Breast Cancer Model ◽  
Cancer Types

AbstractObesity is a disease characterized by chronic low-grade systemic inflammation and has been causally linked to the development of 13 cancer types. Several studies have been undertaken to determine if tumors evolving in obese environments adapt differential interactions with immune cells and if this can be connected to disease outcome. Most of these studies have been limited to single cell lines and tumor models and analysis of limited immune cell populations. Given the multicellular complexity of the immune system and its dysregulation in obesity, we applied high-dimensional suspension mass cytometry to investigate how obesity affects tumor immunity. We used a 36-marker immune-focused mass cytometry panel to interrogate the immune landscape of orthotopic syngeneic mouse models of pancreatic and breast cancer. Unanchored batch correction was implemented to enable simultaneous analysis of tumor cohorts to uncover the immunotypes of each cancer model and reveal remarkably model-specific immune regulation. In the E0771 breast cancer model, we demonstrate an important link to obesity with an increase in two T cell suppressive cell types and a decrease in CD8 T-cells.

scholarly journals Upregulated CCNE1 Correlates with Poor Prognosis, Tumor Immune Infiltration and Escape in Breast Cancer

2021 ◽  
Author(s):  
Jiaxi Feng ◽  
Yanan Hu ◽  
Dan Liu ◽  
Shanshan Wang ◽  
Mengci Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Immune Cells ◽  
Poor Prognosis ◽  
Immune Cell ◽  
High Expression ◽  
Expression Level ◽  
Immune Cell Infiltration ◽  
Immune Infiltration ◽  
Kaplan Meier ◽  
Kaplan Meier Plotter

Abstract Background Breast cancer (BC) is the most common malignant tumor in women and widely known for its poor prognosis. More and more research has discovered that cyclin E1 (CCNE1) plays an important role in progression of various types of cancer. But its specific mechanism in BC progression still needs further research to explore.Methods At first, we determined the expression and prognostic value of CCNE1 through The Cancer Genome Atlas (TCGA) database and The Genotype-Tissue Expression (GTEx) data. Then, we predicted the upstream non-coding RNAs of CCNE1 through StarBase, GEPIA, and Kaplan-Meier plotter database. We further studied the correlation of CCNE1 expression with BC immune cell infiltration, biomarkers of immune cells and immune checkpoints expression through TIMER and GEPIA databases.Results The results suggested that CCNE1 was significantly upregulated in BC and its high expression was correlated with poor prognosis in BC patients. Next, we identified long noncoding RNA (lncRNA) LINC00511 / microRNA-195-5p (miR-195-5p) / CCNE1 axis as the most potential pathway that could regulate CCNE1 expression in BC through StarBase, GEPIA, and Kaplan-Meier plotter database. Furthermore, our in-depth research discovered that CCNE1 expression level was significantly correlated with tumor immune cell infiltration, biomarkers of immune cells, and immune checkpoint expression in BC. conclusions In summary, high expression level of CCNE1 was significantly correlated with poor prognosis, tumor immune infiltration and escape in BC.

scholarly journals A Comparative Analysis of Breast Cancer Detection and Diagnosis Using Data Visualization and Machine Learning Applications

Healthcare ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 111 ◽  
Author(s):  
Muhammet Fatih Ak
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Data Visualization ◽  
Cancer Detection ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Machine Learning Applications ◽  
Cancer Types ◽  
Detection And Diagnosis ◽  
Using Data

In the developing world, cancer death is one of the major problems for humankind. Even though there are many ways to prevent it before happening, some cancer types still do not have any treatment. One of the most common cancer types is breast cancer, and early diagnosis is the most important thing in its treatment. Accurate diagnosis is one of the most important processes in breast cancer treatment. In the literature, there are many studies about predicting the type of breast tumors. In this research paper, data about breast cancer tumors from Dr. William H. Walberg of the University of Wisconsin Hospital were used for making predictions on breast tumor types. Data visualization and machine learning techniques including logistic regression, k-nearest neighbors, support vector machine, naïve Bayes, decision tree, random forest, and rotation forest were applied to this dataset. R, Minitab, and Python were chosen to be applied to these machine learning techniques and visualization. The paper aimed to make a comparative analysis using data visualization and machine learning applications for breast cancer detection and diagnosis. Diagnostic performances of applications were comparable for detecting breast cancers. Data visualization and machine learning techniques can provide significant benefits and impact cancer detection in the decision-making process. In this paper, different machine learning and data mining techniques for the detection of breast cancer were proposed. Results obtained with the logistic regression model with all features included showed the highest classification accuracy (98.1%), and the proposed approach revealed the enhancement in accuracy performances. These results indicated the potential to open new opportunities in the detection of breast cancer.

Absence of the CHEK2*1100delC mutation in non-BRCA1/2 families with multiple cancer types in a high-risk clinic population of Caucasian ancestry

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. 11040-11040
Author(s):  
M. A. Gorin ◽  
M. D. Iniesta ◽  
J. A. Douglas ◽  
K. J. Milliron ◽  
S. D. Merajver
Keyword(s):  
Breast Cancer ◽  
Ovarian Cancer ◽  
North American ◽  
North American Population ◽  
Study Cohort ◽  
Multiple Cancer ◽  
Checkpoint Kinase ◽  
Chek2 1100Delc ◽  
Increased Risk ◽  
Cancer Types

11040 Background: Checkpoint kinase 2(CHEK2) is a cell-cycle-checkpoint kinase that phosphorylates p53 and BRCA1 in response to DNA damage. The contribution of CHEK2 mutations to familial cancer has been widely studied in breast cancer. Most notably, the CHEK2*1100delC mutation has been characterized to confer a 2-fold increased risk for breast cancer in carriers. This finding comes from studies performed on Northern and Eastern European populations. Few studies, however, have been conducted in North American. In contrast to the work done in Europe, these studies suggest a lower frequency of CHEK2*1100delC mutations in breast cancer families. The aim of this study was to determine the frequency of CHEK2*1100delC in members of breast cancer families who tested negative for a deleterious mutation in BRCA1/2. Methods: DNA sequencing was used to genotype 115 individuals for CHEK2*1100delC. Families were characterized by the presence of several cases of breast and/or ovarian cancer and multiple members with other cancers in a single lineage. Given the broad variety of cancers associated with CHEK2 mutations and its function in DNA repair, we hypothesized that these families would be enriched for harboring the CHEK2*1100delC in the germline. Results: No CHEK2*1100delC mutations were detected in 115 individuals, including 39 women diagnosed with breast cancer at an early age, 7 women with bilateral cancer, 2 men with breast cancer and 6 women with ovarian cancer, all of whom were negative for mutations in BRCA1/2.The CHEK2 Breast Cancer Consortium previously reported a frequency of 2.3% for the CHEK2*1100delC mutation among breast cancer cases from families with at least 2 cases of breast cancer (or breast and ovarian cancer) in a first- or second-degree relationship. Based on that, we had approximately 92% power to detect at least one mutation among our study cohort. Conclusions: Our data are consistent with previous reports that suggest a lower frequency of CHEK2*1100delC mutations in North American hereditary breast cancer families without BRCA1/2 mutations and enriched for multiple cancer types. The low frequency of the CHEK2*1100delC in the North American population limits its clinical relevance as a cancer predisposing gene. No significant financial relationships to disclose.

scholarly journals Long non-coding RNA FAM83H-AS1 acts as a potential oncogenic driver in human ovarian cancer

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Xiaolei Yuan ◽  
Ying Huang ◽  
Man Guo ◽  
Xiaowei Hu ◽  
Peiling Li
Keyword(s):  
Ovarian Cancer ◽  
Normal Control ◽  
Immune Cell ◽  
Multiple Cancer ◽  
Non Coding Rna ◽  
Incidence And Mortality ◽  
Cancer Types ◽  
Oncogenic Driver ◽  
Therapy Target ◽  
Long Non Coding Rna

Abstract Objective Ovarian cancer (OC) is one of the most aggressive women cancers with increasing incidence and mortality rates worldwide. Long non-coding RNAs (lncRNAs) could as major players in OC process. Although FAM83H antisense RNA1 (FAM83H-AS1) is demonstrated play an important roles in a many cancers, the detailed function and mechanism has not been reported in OC. Results We integrated multiple kinds of bioinformatics approaches and experiments validated method to evaluate functions of FAM83H-AS1 in OC. Some differential expressed lncRNAs were identified between OC and normal control tissues. FAM83H-AS1 was one of most differentially expressed lncRNAs and up-regulated in multiple cancer types. Specially, expression of FAM83H-AS1 was higher in OC and showed difference in diverse stages. High FAM83H-AS1 expression is associated with worse pan-cancer and OC outcomes. FAM83H-AS1-centric network including lncRNA-miRNA, lncRNA-protein and lncRNA-mRNA ceRNA network were constructed to infer the function and mechanism of FAM83H-AS1. There were two methylation sites including cg01399317 and cg20519035 located at FAM83H-AS1. The methylation level of cg01399317 was correlated with gene expression of FAM83H-AS1. The expression level of FAM83H-AS1 was correlated with infiltration level of immune cell including macrophage, neutrphil and dendritic cell in OC patients. Lastly, qRT-PCR showed that the expression of FAM83H-AS1 was higher in OC tissues than normal control tissues. Conclusion Collectively, these results indicated that FAM83H-AS1 may act as an oncogenic driver and it may be a potential therapy target in OC.

scholarly journals Long non-coding RNA FAM83H-AS1 acts as an oncogenic driver in human ovarian cancer

2020 ◽  
Author(s):  
Xiaolei Yuan ◽  
Ying Huang ◽  
Man Guo ◽  
Xiaowei Hu ◽  
Peiling Li
Keyword(s):  
Ovarian Cancer ◽  
Normal Control ◽  
Immune Cell ◽  
Multiple Cancer ◽  
Non Coding Rna ◽  
Incidence And Mortality ◽  
Cancer Types ◽  
Oncogenic Driver ◽  
Therapy Target ◽  
Long Non Coding Rna

Abstract Background Ovarian cancer (OC) is one of the most aggressive women cancers with increasing incidence and mortality rates worldwide. Long non-coding RNAs (lncRNAs) could as major players in OC process. Although FAM83H antisense RNA1 (FAM83H-AS1) is demonstrated play an important roles in a many cancers, the detailed function and mechanism has not been reported in OC. Methods We integrated multiple kinds of bioinformatics approaches and experiments validated method to evaluate functions of FAM83H-AS1 in OC. Results Some differential expressed lncRNAs were identified between OC and normal control tissues. FAM83H-AS1 was one of most differentially expressed lncRNAs and up-regulated in multiple cancer types. Specially, expression of FAM83H-AS1 was higher in OC and showed difference in diverse stages. High FAM83H-AS1 expression is associated with worse pan-cancer and OC outcomes. FAM83H-AS1-centric network including lncRNA-miRNA, lncRNA-protein and lncRNA-mRNA ceRNA network were constructed to infer the function and mechanism of FAM83H-AS1. There were two methylation sites including cg01399317 and cg20519035 located at FAM83H-AS1. The methylation level of cg01399317 was correlated with gene expression of FAM83H-AS1. The expression level of FAM83H-AS1 was correlated with infiltration level of immune cell including macrophage, neutrphil and dendritic cell in OC patients. Lastly, qRT-PCR showed that the expression of FAM83H-AS1 was higher in OC tissues than normal control tissues. Conclusions Collectively, these results indicated that FAM83H-AS1 may act as an oncogenic driver and it may be a potential therapy target in OC.

scholarly journals Upregulated CCNE1 Correlates with Poor Prognosis, Tumor Immune Infiltration and Escape in Breast Cancer

2021 ◽  
Author(s):  
Jiaxi Feng ◽  
Yanan Hu ◽  
Dan Liu ◽  
Shanshan Wang ◽  
Mengci Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Immune Cells ◽  
Poor Prognosis ◽  
Immune Cell ◽  
High Expression ◽  
Expression Level ◽  
Immune Cell Infiltration ◽  
Immune Infiltration ◽  
Kaplan Meier ◽  
Kaplan Meier Plotter

Abstract BackgroundBreast cancer (BC) is the most common malignant tumor in women and widely known for its poor prognosis. More and more research has discovered that cyclin E1 (CCNE1) plays an important role in progression of various types of cancer. But its specific mechanism in BC progression still needs further research to explore.MethodsAt first, we determined the expression and prognostic value of CCNE1 through The Cancer Genome Atlas (TCGA) database and The Genotype-Tissue Expression (GTEx) data. Then, we predicted the upstream non-coding RNAs of CCNE1 through StarBase, GEPIA, and Kaplan-Meier plotter database. We further studied the correlation of CCNE1 expression with BC immune cell infiltration, biomarkers of immune cells and immune checkpoints expression through TIMER and GEPIA databases.ResultsThe results suggested that CCNE1 was significantly upregulated in BC and its high expression was correlated with poor prognosis in BC patients. Next, we identified long noncoding RNA (lncRNA) LINC00511 / microRNA-195-5p (miR-195-5p) / CCNE1 axis as the most potential pathway that could regulate CCNE1 expression in BC through StarBase, GEPIA, and Kaplan-Meier plotter database. Furthermore, our in-depth research discovered that CCNE1 expression level was significantly correlated with tumor immune cell infiltration, biomarkers of immune cells, and immune checkpoint expression in BC.ConclusionIn summary, high expression level of CCNE1 was significantly correlated with poor prognosis, tumor immune infiltration and escape in BC.

scholarly journals High dimensional immunotyping of tumors grown in obese and non-obese mice

2021 ◽  
pp. dmm.048977
Author(s):  
Cara E. Wogsland ◽  
Hilde E. Lien ◽  
Line Pedersen ◽  
Pahul Hanjra ◽  
Sturla M. Grondal ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Immune Cell ◽  
Cell Types ◽  
High Dimensional ◽  
Low Grade ◽  
Cancer Model ◽  
Mass Cytometry ◽  
Batch Correction ◽  
Breast Cancer Model ◽  
Cancer Types

Obesity is a disease characterized by chronic low-grade systemic inflammation and has been causally linked to the development of 13 cancer types. Several studies have been undertaken to determine if tumors evolving in obese environments adapt differential interactions with immune cells and if this can be connected to disease outcome. Most of these studies have been limited to single cell lines and tumor models and analysis of limited immune cell populations. Given the multicellular complexity of the immune system and its dysregulation in obesity, we applied high-dimensional suspension mass cytometry to investigate how obesity affects tumor immunity. We used a 36-marker immune-focused mass cytometry panel to interrogate the immune landscape of orthotopic syngeneic mouse models of pancreatic and breast cancer. Unanchored batch correction was implemented to enable simultaneous analysis of tumor cohorts to uncover the immunotypes of each cancer model and reveal remarkably model-specific immune regulation. In the E0771 breast cancer model, we demonstrate an important link to obesity with an increase in two T cell suppressive cell types and a decrease in CD8 T-cells.

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