scholarly journals Identifying a Clinically Applicable Mutational Burden Threshold as a Potential Biomarker of Response to Immune Checkpoint Therapy in Solid Tumors

2017 ◽  
pp. 1-13 ◽  
Author(s):  
Anshuman Panda ◽  
Anil Betigeri ◽  
Kalyanasundaram Subramanian ◽  
Jeffrey S. Ross ◽  
Dean C. Pavlick ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Immune Checkpoint ◽  
Immune Checkpoint Blockade ◽  
The Cancer Genome Atlas ◽  
Checkpoint Activation ◽  
Mutational Burden ◽  
Whole Exome ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Genome Atlas

Purpose An association between mutational burden and response to immune checkpoint therapy has been documented in several cancer types. The potential for such a mutational burden threshold to predict response to immune checkpoint therapy was evaluated in several clinical datasets, where mutational burden was measured either by whole-exome sequencing or by using commercially available sequencing panels. Methods Whole-exome sequencing and RNA sequencing data of 33 solid cancer types from The Cancer Genome Atlas were analyzed to determine whether a robust immune checkpoint–activating mutation (iCAM) burden threshold associated with evidence of immune checkpoint activation exists in these cancers that may serve as a biomarker of response to immune checkpoint blockade therapy. Results We found that a robust iCAM threshold, associated with signatures of immune checkpoint activation, exists in eight of 33 solid cancers: melanoma, lung adenocarcinoma, colon adenocarcinoma, endometrial cancer, stomach adenocarcinoma, cervical cancer, estrogen receptor–positive/human epidermal growth factor receptor 2–negative breast cancer, and bladder-urothelial cancer. Tumors with a mutational burden higher than the threshold (iCAM positive) also had clear histologic evidence of lymphocytic infiltration. In published datasets of melanoma, lung adenocarcinoma, and colon cancer, patients with iCAM-positive tumors had significantly better response to immune checkpoint therapy compared with those with iCAM-negative tumors. Receiver operating characteristic analysis using The Cancer Genome Atlas predictions as the gold standard showed that iCAM-positive tumors are accurately identifiable using clinical sequencing assays, such as FoundationOne (Foundation Medicine, Cambridge, MA) or StrandAdvantage (Strand Life Sciences, Bangalore, India). Using the FoundationOne-derived threshold, an analysis of 113 melanoma tumors showed that patients with iCAM-positive disease have significantly better response to immune checkpoint therapy. iCAM-positive and iCAM-negative tumors have distinct mutation patterns and different immune microenvironments. Conclusion In eight solid cancers, a mutational burden threshold exists that may predict response to immune checkpoint blockade. This threshold is identifiable using available clinical sequencing assays.

scholarly journals Epstein-Barr Virus-Positive Cancers Show Altered B-Cell Clonality

mSystems ◽  
2018 ◽  
Vol 3 (5) ◽  
Author(s):  
Sara R. Selitsky ◽  
David Marron ◽  
Lisle E. Mose ◽  
Joel S. Parker ◽  
Dirk P. Dittmer
Keyword(s):  
B Cells ◽  
B Cell ◽  
Epstein Barr Virus ◽  
The Cancer Genome Atlas ◽  
Barr Virus ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Epstein Barr ◽  
Tumor Types ◽  
Genome Atlas

ABSTRACTEpstein-Barr virus (EBV) is convincingly associated with gastric cancer, nasopharyngeal carcinoma, and certain lymphomas, but its role in other cancer types remains controversial. To test the hypothesis that there are additional cancer types with high prevalence of EBV, we determined EBV viral expression in all the Cancer Genome Atlas Project (TCGA) mRNA sequencing (mRNA-seq) samples (n= 10,396) from 32 different tumor types. We found that EBV was present in gastric adenocarcinoma and lymphoma, as expected, and was also present in >5% of samples in 10 additional tumor types. For most samples, EBV transcript levels were low, which suggests that EBV was likely present due to infected infiltrating B cells. In order to determine if there was a difference in the B-cell populations, we assembled B-cell receptors for each sample and found B-cell receptor abundance (P≤ 1.4 × 10−20) and diversity (P≤ 8.3 × 10−27) were significantly higher in EBV-positive samples. Moreover, diversity was independent of B-cell abundance, suggesting that the presence of EBV was associated with an increased and altered B-cell population.IMPORTANCEAround 20% of human cancers are associated with viruses. Epstein-Barr virus (EBV) contributes to gastric cancer, nasopharyngeal carcinoma, and certain lymphomas, but its role in other cancer types remains controversial. We assessed the prevalence of EBV in RNA-seq from 32 tumor types in the Cancer Genome Atlas Project (TCGA) and found EBV to be present in >5% of samples in 12 tumor types. EBV infects epithelial cells and B cells and in B cells causes proliferation. We hypothesized that the low expression of EBV in most of the tumor types was due to infiltration of B cells into the tumor. The increase in B-cell abundance and diversity in subjects where EBV was detected in the tumors strengthens this hypothesis. Overall, we found that EBV was associated with an increased and altered immune response. This result is not evidence of causality, but a potential novel biomarker for tumor immune status.

scholarly journals Utility of a custom designed next generation DNA sequencing gene panel to molecularly classify endometrial cancers according to The Cancer Genome Atlas subgroups

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Eirwen M. Miller ◽  
Nicole E. Patterson ◽  
Gregory M. Gressel ◽  
Rouzan G. Karabakhtsian ◽  
Michal Bejerano-Sagie ◽  
...  
Keyword(s):  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Targeted Sequencing ◽  
Molecular Subgroups ◽  
Mutational Burden ◽  
Tumor Mutational Burden ◽  
Cancer Genome Atlas ◽  
Endometrial Cancers ◽  
Grade 3 ◽  
Genome Atlas

Abstract Background The Cancer Genome Atlas identified four molecular subgroups of endometrial cancer with survival differences based on whole genome, transcriptomic, and proteomic characterization. Clinically accessible algorithms that reproduce this data are needed. Our aim was to determine if targeted sequencing alone allowed for molecular classification of endometrial cancer. Methods Using a custom-designed 156 gene panel, we analyzed 47 endometrial cancers and matching non-tumor tissue. Variants were annotated for pathogenicity and medical records were reviewed for the clinicopathologic variables. Using molecular characteristics, tumors were classified into four subgroups. Group 1 included patients with > 570 unfiltered somatic variants, > 9 cytosine to adenine nucleotide substitutions per sample, and < 1 cytosine to guanine nucleotide substitution per sample. Group 2 included patients with any somatic mutation in MSH2, MSH6, MLH1, PMS2. Group 3 included patients with TP53 mutations without mutation in mismatch repair genes. Remaining patients were classified as group 4. Analyses were performed using SAS 9.4 (SAS Institute Inc., Cary, North Carolina, USA). Results Endometrioid endometrial cancers had more candidate variants of potential pathogenic interest (median 6 IQR 4.13 vs. 2 IQR 2.3; p < 0.01) than uterine serous cancers. PTEN (82% vs. 15%, p < 0.01) and PIK3CA (74% vs. 23%, p < 0.01) mutations were more frequent in endometrioid than serous carcinomas. TP53 (18% vs. 77%, p < 0.01) mutations were more frequent in serous carcinomas. Visual inspection of the number of unfiltered somatic variants per sample identified six grade 3 endometrioid samples with high tumor mutational burden, all of which demonstrated POLE mutations, most commonly P286R and V411L. Of the grade 3 endometrioid carcinomas, those with POLE mutations were less likely to have risk factors necessitating adjuvant treatment than those with low tumor mutational burden. Targeted sequencing was unable to assign samples to microsatellite unstable, copy number low, and copy number high subgroups. Conclusions Targeted sequencing can predict the presence of POLE mutations based on the tumor mutational burden. However, targeted sequencing alone is inadequate to classify endometrial cancers into molecular subgroups identified by The Cancer Genome Atlas.

scholarly journals METTL3-Mediated m6A mRNA Modification of FBXW7 Suppresses Lung Adenocarcinoma

2020 ◽  
Author(s):  
Yingtong Wu ◽  
Ning Chang ◽  
Yong Zhang ◽  
Xinxin Zhang ◽  
Leidi Xu ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Cell Model ◽  
Biological Effects ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Luciferase Reporter ◽  
Rna Immunoprecipitation ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Abstract BackgroundFBXW7 m6A modification plays an important role in lung adenocarcinoma (LUAD) progression; however, the underlying mechanisms remain unclear.MethodsThe correlation between FBXW7 and various genes related to m6A modification was analyzed using The Cancer Genome Atlas database. The regulatory effects of METTL3 on FBXW7 mRNA m6A modification were examined in a cell model, and the underlying mechanism was determined by methylated RNA immunoprecipitation, RNA immunoprecipitation, luciferase reporter, and mutagenesis assays. In vitro experiments were performed to further explore the biological effects of METTL3-mediated FBXW7 m6A modification on LUAD development.ResultsDecreased FBXW7 expression was accompanied by downregulated METTL3 expression in human LUAD tissues and was associated with a worse prognosis for LUAD in The Cancer Genome Atlas database. m6A was highly enriched in METTL3-mediated FBXW7 transcripts, and increased m6A modification in the coding sequence region increased its translation. Functionally, METTL3 overexpression or knockdown affected the apoptosis and proliferation phenotype of LUAD cells by regulating FBXW7 m6A modification and expression. Furthermore, FBXW7 overexpression in METTL3-depleted cells partially restored the suppression of LUAD cells in vitro and in vivo.ConclusionsOur findings reveal that METTL3 positively regulates FBXW7 expression and confirm the tumor-suppressive role of m6A-modified FBXW7, thus providing insight into its epigenetic regulatory mechanisms in LUAD initiation and development.

scholarly journals The PD-L1 Metabolic Interactome Intersects with Choline Metabolism and Inflammation

2020 ◽  
Author(s):  
Jesús Pacheco-Torres ◽  
Marie-France Penet ◽  
Yelena Mironchik ◽  
Balaji Krishnamachary ◽  
Zaver M Bhujwalla
Keyword(s):  
Breast Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Immune Checkpoint ◽  
Cancer Metabolism ◽  
The Cancer Genome Atlas ◽  
Immune Checkpoints ◽  
Cancer Genome Atlas ◽  
Cox 2 ◽  
Genome Atlas

Abstract Background: Harnessing the power of the immune system by using immune checkpoint inhibitors has resulted in some of the most exciting advances in cancer treatment. The full potential of this approach has, however, not been fully realized for treating many cancers such as pancreatic and breast cancer. Cancer metabolism influences many aspects of cancer progression including immune surveillance. An expanded understanding of how cancer metabolism can directly impact immune checkpoints may allow further optimization of immunotherapy. We therefore investigated, for the first time, the relationship between the overexpression of choline kinase-α (Chk-α), an enzyme observed in most cancers, and expression of the immune checkpoint PD-L1. Methods: We used small interfering RNA to downregulate Chk-a, PD-L1 or both in two triple negative human breast cancer cell lines (MDA-MB-231 and SUM-149) and two human pancreatic ductal adenocarcinoma cell lines (Pa09C and Pa20C). The effects of the downregulation were studied at the genomic, proteomic and metabolomic levels. The findings were compared with results obtained by analysis of public data from The Cancer Genome Atlas Program.Results: We identified an inverse dependence between Chk-α and PD-L1 at the genomic, proteomic and metabolomic levels. We also found that prostaglandin-endoperoxide synthase 2 (COX-2) and transforming growth factor beta (TGF-β) play an important role in this relationship. We independently confirmed this relationship in human cancers by analyzing data from The Cancer Genome Atlas Program. Conclusions: Our data identified previously unknown roles of PD-L1 in cancer cell metabolic reprogramming, and revealed the immunosuppressive increased PD-L1 effect of Chk-α downregulation. These data suggest that PD-L1 regulation of metabolism may be mediated through Chk-α, COX-2 and TGF-β. The observations provide new insights that can be applied to the rational design of combinatorial therapies targeting immune checkpoints and cancer metabolism.

scholarly journals The PD-L1 metabolic interactome intersects with choline metabolism and inflammation

2021 ◽  
Author(s):  
Jesús Pacheco-Torres ◽  
Marie-France Penet ◽  
Yelena Mironchik ◽  
Balaji Krishnamachary ◽  
Zaver M Bhujwalla
Keyword(s):  
Breast Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Immune Checkpoint ◽  
Cancer Metabolism ◽  
The Cancer Genome Atlas ◽  
Immune Checkpoints ◽  
Cancer Genome Atlas ◽  
Cox 2 ◽  
Genome Atlas

Abstract Background: Harnessing the power of the immune system by using immune checkpoint inhibitors has resulted in some of the most exciting advances in cancer treatment. The full potential of this approach has, however, not been fully realized for treating many cancers such as pancreatic and breast cancer. Cancer metabolism influences many aspects of cancer progression including immune surveillance. An expanded understanding of how cancer metabolism can directly impact immune checkpoints may allow further optimization of immunotherapy. We therefore investigated, for the first time, the relationship between the overexpression of choline kinase-α (Chk-α), an enzyme observed in most cancers, and expression of the immune checkpoint PD-L1. Methods: We used small interfering RNA to downregulate Chk-a, PD-L1 or both in two triple negative human breast cancer cell lines (MDA-MB-231 and SUM-149) and two human pancreatic ductal adenocarcinoma cell lines (Pa09C and Pa20C). The effects of the downregulation were studied at the genomic, proteomic and metabolomic levels. The findings were compared with results obtained by analysis of public data from The Cancer Genome Atlas Program.Results: We identified an inverse dependence between Chk-α and PD-L1 at the genomic, proteomic and metabolomic levels. We also found that prostaglandin-endoperoxide synthase 2 (COX-2) and transforming growth factor beta (TGF-β) play an important role in this relationship. We independently confirmed this relationship in human cancers by analyzing data from The Cancer Genome Atlas Program. Conclusions: Our data identified previously unknown roles of PD-L1 in cancer cell metabolic reprogramming, and revealed the immunosuppressive increased PD-L1 effect of Chk-α downregulation. These data suggest that PD-L1 regulation of metabolism may be mediated through Chk-α, COX-2 and TGF-β. The observations provide new insights that can be applied to the rational design of combinatorial therapies targeting immune checkpoints and cancer metabolism.

scholarly journals Characterizing Cancer-Specific Networks by Integrating TCGA Data

2014 ◽  
Vol 13s2 ◽  
pp. CIN.S13776
Author(s):  
Yanxun Xu ◽  
Yitan Zhu ◽  
Peter Müller ◽  
Riten Mitra ◽  
Yuan Ji
Keyword(s):  
Graphical Model ◽  
Biological Network ◽  
The Cancer Genome Atlas ◽  
Genomic Features ◽  
Conditional Dependence ◽  
Copy Numbers ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Dna Copy Numbers ◽  
Genome Atlas

The Cancer Genome Atlas (TCGA) generates comprehensive genomic data for thousands of patients over more than 20 cancer types. TCGA data are typically whole-genome measurements of multiple genomic features, such as DNA copy numbers, DNA methylation, and gene expression, providing unique opportunities for investigating cancer mechanism from multiple molecular and regulatory layers. We propose a Bayesian graphical model to systemically integrate multi-platform TCGA data for inference of the interactions between different genomic features either within a gene or between multiple genes. The presence or absence of edges in the graph indicates the presence or absence of conditional dependence between genomic features. The inference is restricted to genes within a known biological network, but can be extended to any sets of genes. Applying the model to the same genes using patient samples in two different cancer types, we identify network components that are common as well as different between cancer types. The examples and codes are available at https://www.ma.utexas.edu/users/yxu/software.html .

scholarly journals The PD-L1 metabolic interactome intersects with choline metabolism and inflammation

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Jesus Pacheco-Torres ◽  
Marie-France Penet ◽  
Yelena Mironchik ◽  
Balaji Krishnamachary ◽  
Zaver M. Bhujwalla
Keyword(s):  
Breast Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Immune Checkpoint ◽  
Cancer Metabolism ◽  
The Cancer Genome Atlas ◽  
Immune Checkpoints ◽  
Cancer Genome Atlas ◽  
Cox 2 ◽  
Genome Atlas

Abstract Background Harnessing the power of the immune system by using immune checkpoint inhibitors has resulted in some of the most exciting advances in cancer treatment. The full potential of this approach has, however, not been fully realized for treating many cancers such as pancreatic and breast cancer. Cancer metabolism influences many aspects of cancer progression including immune surveillance. An expanded understanding of how cancer metabolism can directly impact immune checkpoints may allow further optimization of immunotherapy. We therefore investigated, for the first time, the relationship between the overexpression of choline kinase-α (Chk-α), an enzyme observed in most cancers, and the expression of the immune checkpoint PD-L1. Methods We used small interfering RNA to downregulate Chk-α, PD-L1, or both in two triple-negative human breast cancer cell lines (MDA-MB-231 and SUM-149) and two human pancreatic ductal adenocarcinoma cell lines (Pa09C and Pa20C). The effects of the downregulation were studied at the genomic, proteomic, and metabolomic levels. The findings were compared with the results obtained by the analysis of public data from The Cancer Genome Atlas Program. Results We identified an inverse dependence between Chk-α and PD-L1 at the genomic, proteomic, and metabolomic levels. We also found that prostaglandin-endoperoxide synthase 2 (COX-2) and transforming growth factor beta (TGF-β) play an important role in this relationship. We independently confirmed this relationship in human cancers by analyzing data from The Cancer Genome Atlas Program. Conclusions Our data identified previously unknown roles of PD-L1 in cancer cell metabolic reprogramming, and revealed the immunosuppressive increased PD-L1 effect of Chk-α downregulation. These data suggest that PD-L1 regulation of metabolism may be mediated through Chk-α, COX-2, and TGF-β. The observations provide new insights that can be applied to the rational design of combinatorial therapies targeting immune checkpoints and cancer metabolism.

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