scholarly journals Development and Performance of a CD8 Gene Signature for Characterizing Inflammation in the Tumor Microenvironment Across Multiple Tumor Types

2021 ◽  
Author(s):  
Peter M. Szabo ◽  
Saumya Pant ◽  
Scott Ely ◽  
Keyur Desai ◽  
Esperanza Anguiano ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Gene Signature ◽  
Multiple Tumor ◽  
And Performance ◽  
Tumor Types

Evaluation of tumor microenvironment and biomarkers of immune checkpoint inhibitor (ICI) response in metastatic renal cell carcinoma (mRCC).

2019 ◽  
Vol 37 (8_suppl) ◽  
pp. 63-63
Author(s):  
Jason Zhu ◽  
Sarabjot Pabla ◽  
Matthew Labriola ◽  
Rajan T. Gupta ◽  
Shannon McCall ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Standard Of Care ◽  
Reference Population ◽  
Gene Signature ◽  
Multiple Tumor ◽  
Markers Of Inflammation ◽  
Expert Radiologist ◽  
Chi Squared ◽  
Ffpe Tumor ◽  
Tumor Types

63 Background: ICIs are now standard of care for mRCC; however, there are few biomarkers to predict ICI response. Recent data from atezolizumab/bevacizumab trials in mRCC suggest tumors with high Teffhigh/PD-L1+ are more likely to respond to ICI. Here, we use this Teff gene panel as well as other markers of inflammation in the tumor microenvironment to correlate with ICI responses. Methods: This multicenter study evaluated 69 pts with mRCC treated with ICIs. FFPE tumor samples were evaluated by RNA sequencing to measure transcript levels of genes related Teff status. Teff status was defined as the mRNA expression of 17 genes (CD8, CD27, IFNG, GZMA, GZMB, PRF1, EOMES, CXCL9, CXCL10, CXCL11, CD274, CTLA4, FOXP3, TIGIT, IDO1, PSMB9, TAP1), with Teffhigh/low separated at the median. PD-L1 positivity was defined as ≥1% TPS based on Dako 22C3 IHC assay, and TMB high as > 10 mutations per megabase. Inflamed tumors were defined as CD8 expression in the top 75th percentile compared to a large reference population of multiple tumor types. Best responses to ICI was determined by an expert radiologist using RECIST 1.1 criteria. Inflamed tumor status, Teff gene expression, PD-L1 positive, and TMB were associated with disease control (DC, defined as CR, PR, or stable disease). DC comparisons were tested using a chi-squared test with Yates’s continuity correction. Results: DC was 63% (5/8) amongst PD-L1 positive pts and 52% (31/60) in PD-L1 negative patients (p = 0.84). Only 2 pts were TMB high. The majority of mRCC tumors (97%, 67/69) were TMB low. 6-month DC in TMB high tumors was 50% (1/2) and 49.3% (33/67) in TMB low tumors (p = 1.0). 36 pts were classified as Teffhigh and 33 patients were classified as Tefflow. 6-month DC was 61% (22/36) in the Teffhigh cohort and 36% (12/33) in the Tefflow cohort (p = 0.069). 6-month DC was 64% of inflamed tumors (16/25) vs 41% of non-inflamed tumors (18/44) (p = 0.111). Conclusions: TMB high and PD-L1 expression do not reliably predict for DC in pts with mRCC. Utilizing a gene signature score may better predict ICI response.

Evaluation of tumor microenvironment and biomarkers of immune checkpoint inhibitor (ICI) response in metastatic renal cell carcinoma (mRCC).

2019 ◽  
Vol 37 (7_suppl) ◽  
pp. 607-607 ◽  
Author(s):  
Jason Zhu ◽  
Sarabjot Pabla ◽  
Matthew Labriola ◽  
Rajan T. Gupta ◽  
Shannon McCall ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Standard Of Care ◽  
Reference Population ◽  
Gene Signature ◽  
Multiple Tumor ◽  
Markers Of Inflammation ◽  
Expert Radiologist ◽  
Chi Squared ◽  
Ffpe Tumor ◽  
Tumor Types

607 Background: ICIs are now standard of care for mRCC; however, there are few biomarkers to predict ICI response. Recent data from atezolizumab/bevacizumab trials in mRCC suggest tumors with high Teffhigh/PD-L1+ are more likely to respond to ICI. Here, we use this Teff gene panel as well as other markers of inflammation in the tumor microenvironment to correlate with ICI responses. Methods: This multicenter study evaluated 69 pts with mRCC treated with ICIs. FFPE tumor samples were evaluated by RNA sequencing to measure transcript levels of genes related Teff status. Teff status was defined as the mRNA expression of 17 genes (CD8, CD27, IFNG, GZMA, GZMB, PRF1, EOMES, CXCL9, CXCL10, CXCL11, CD274, CTLA4, FOXP3, TIGIT, IDO1, PSMB9, TAP1), with Teffhigh/low separated at the median. PD-L1 positivity was defined as ≥1% TPS based on Dako 22C3 IHC assay, and TMB high as > 10 mutations per megabase. Inflamed tumors were defined as CD8 expression in the top 75th percentile compared to a large reference population of multiple tumor types. Best responses to ICI was determined by an expert radiologist using RECIST 1.1 criteria. Inflamed tumor status, Teff gene expression, PD-L1 positive, and TMB were associated with disease control (DC, defined as CR, PR, or stable disease). DC comparisons were tested using a chi-squared test with Yates’s continuity correction. Results: DC was 63% (5/8) amongst PD-L1 positive pts and 52% (31/60) in PD-L1 negative patients (p = 0.84). Only 2 pts were TMB high. The majority of mRCC tumors (97%, 67/69) were TMB low. 6-month DC in TMB high tumors was 50% (1/2) and 49.3% (33/67) in TMB low tumors (p = 1.0). 36 pts were classified as Teffhigh and 33 patients were classified as Tefflow. 6-month DC was 61% (22/36) in the Teffhigh cohort and 36% (12/33) in the Tefflow cohort (p = 0.069). 6-month DC was 64% of inflamed tumors (16/25) vs 41% of non-inflamed tumors (18/44) (p = 0.111). Conclusions: TMB high and PD-L1 expression do not reliably predict for DC in pts with mRCC. Utilizing a gene signature score may better predict ICI response.

scholarly journals CCL2: An Important Mediator Between Tumor Cells and Host Cells in Tumor Microenvironment

2021 ◽  
Vol 11 ◽  
Author(s):  
Jiakang Jin ◽  
Jinti Lin ◽  
Ankai Xu ◽  
Jianan Lou ◽  
Chao Qian ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Cell Types ◽  
Host Cells ◽  
Specific Cell ◽  
Tumor Patients ◽  
Multiple Tumor ◽  
Inflammatory Monocytes ◽  
Immunosuppressive Cell ◽  
Tumor Types

Tumor microenvironment (TME) formation is a major cause of immunosuppression. The TME consists of a considerable number of macrophages and stromal cells that have been identified in multiple tumor types. CCL2 is the strongest chemoattractant involved in macrophage recruitment and a powerful initiator of inflammation. Evidence indicates that CCL2 can attract other host cells in the TME and direct their differentiation in cooperation with other cytokines. Overall, CCL2 has an unfavorable effect on prognosis in tumor patients because of the accumulation of immunosuppressive cell subtypes. However, there is also evidence demonstrating that CCL2 enhances the anti-tumor capability of specific cell types such as inflammatory monocytes and neutrophils. The inflammation state of the tumor seems to have a bi-lateral role in tumor progression. Here, we review works focusing on the interactions between cancer cells and host cells, and on the biological role of CCL2 in these processes.

Association of an inflammatory gene signature with CD8 expression by immunohistochemistry (IHC) in multiple tumor types.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 2593-2593
Author(s):  
Peter M Szabo ◽  
Zhenhao Qi ◽  
Kim Zerba ◽  
Scott Ely ◽  
Robin Edwards ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Linear Models ◽  
Predictive Accuracy ◽  
Statistical Modelling ◽  
Gene Signature ◽  
Specific Gene ◽  
Multiple Tumor ◽  
Technical Errors ◽  
Tumor Inflammation ◽  
Tumor Types

2593 Background: A multiparameter tumor inflammation assay based on gene expression profiling (TIA-GEP) can extend the utility of IHC to interrogate the tumor microenvironment (TME). Using CD8 expression assessed by IHC (CD8-IHC) as a surrogate for inflammation, statistical modelling was used to develop a specific gene signature on the TIA-GEP panel to predict CD8-IHC. The correlation between TIA-GEP and CD8-IHC and the prevalence of inflammation were explored across multiple tumor types. Methods: Levels of inflammation were measured by CD8-IHC and TIA-GEP on 1778 procured samples across 12 tumor types. Quality control metrics involved sample input quality, technical errors, and inter-run variability. Generalized linear models were used to identify an inflammation score that predicts the CD8-IHC score in melanoma and SCCHN tissue. The predictive accuracy of this signature was also examined in 10 additional tumor types. Results: Assessment of TME inflammation by CD8-IHC was consistent with that observed by TIA-GEP in multiple tumor types. The range of inflammation varied across different tumor types, with relatively lower inflammation range and scores in SCLC, ovarian, and prostate cancers, and higher values in NSCLC, melanoma, SCCHN, and gastric cancers. R2 x 100 values reflecting percent variation in CD8-IHC associated with TIA-GEP ranged from 62.4% to 79.2% ( P < 0.0001) for all tumor types except prostate cancer (32.5%). Low correlation in prostate cancer may be a result of low prevalence of inflammation by CD8-IHC. Estimated linear regression slopes between CD8-IHC and TIA-GEP ranged from 0.74 in SCLC to 1.27 in gastric cancer. Conclusions: The results suggest that the inflammation signature is a robust potential diagnostic tool predicting inflammation in the TME. The inflammation signature not only correlates with CD8-IHC for multiple tumor types, but also leverages the alternative benefits associated with TIA-GEP, which include information related to tumor inflammation-associated biomarkers and flexibility in exploring the value of other genomic signatures.

scholarly journals Analysis of genetic signatures of tumor microenvironment yields insight into mechanisms of resistance to immunotherapy

2020 ◽  
Author(s):  
Ben Wang ◽  
Mengmeng Liu ◽  
Zhujie Ran ◽  
Xin Li ◽  
Jie Li ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Signaling Pathways ◽  
Tumor Patients ◽  
Multiple Tumor ◽  
Ppar Signaling ◽  
Tumor Types ◽  
Genetic Signatures ◽  
Chemotherapy Agents ◽  
Multiple Signaling ◽  
Insight Into

AbstractBackgroundTherapeutic intervention targeting immune cells have led to remarkable improvements in clinical outcomes of tumor patients. However, responses are not universal. The inflamed tumor microenvironment has been reported to correlate with response in tumor patients. However, due to the lack of appropriate experimental methods, the reason why the immunotherapeutic resistance still existed on the inflamed tumor microenvironment remains unclear.Materials and methodsHere, based on integrated single-cell RNA sequencing technology, we classified tumor microenvironment into inflamed immunotherapeutic responsive and inflamed non-responsive. Then, phenotype-specific genes were identified to show mechanistic differences between distant TME phenotypes. Finally, we screened for some potential favorable TME phenotypes transformation drugs to aid current immunotherapy.ResultsMultiple signaling pathways were phenotypes-specific dysregulated. For example, Interleukin signaling pathways including IL-4 and IL-13 were activated in inflamed TME across multiple tumor types. PPAR signaling pathways and multiple epigenetic pathways were respectively inhibited and activated in inflamed immunotherapeutic non-responsive TME, suggesting a potential mechanism of immunotherapeutic resistance and target for therapy. We also identified some genetic markers of inflamed non-responsive or responsive TME, some of which have shown its potentials to enhance the efficacy of current immunotherapy.ConclusionThese results may contribute to the mechanistic understanding of immunotherapeutic resistance and guide rational therapeutic combinations of distant targeted chemotherapy agents with immunotherapy.

scholarly journals The PTEN pathway in Tregs is a critical driver of the suppressive tumor microenvironment

2015 ◽  
Vol 1 (10) ◽  
pp. e1500845 ◽  
Author(s):  
Madhav D. Sharma ◽  
Rahul Shinde ◽  
Tracy L. McGaha ◽  
Lei Huang ◽  
Rikke B. Holmgaard ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Phosphatase And Tensin Homolog ◽  
Effector Cells ◽  
Multiple Tumor ◽  
Tensin Homolog ◽  
Lipid Phosphatase ◽  
Immunosuppressive Tumor Microenvironment ◽  
Specialized Form ◽  
Tumor Types ◽  
Inflammatory Milieu

The tumor microenvironment is profoundly immunosuppressive. We show that multiple tumor types create intratumoral immune suppression driven by a specialized form of regulatory T cell (Treg) activation dependent on the PTEN (phosphatase and tensin homolog) lipid phosphatase. PTEN acted to stabilize Tregs in tumors, preventing them from reprogramming into inflammatory effector cells. In mice with a Treg-specific deletion of PTEN, tumors grew slowly, were inflamed, and could not create an immunosuppressive tumor microenvironment. In normal mice, exposure to apoptotic tumor cells rapidly elicited PTEN-expressing Tregs, and PTEN-deficient mice were unable to maintain tolerance to apoptotic cells. In wild-type mice with large established tumors, pharmacologic inhibition of PTEN after chemotherapy or immunotherapy profoundly reconfigured the tumor microenvironment, changing it from a suppressive to an inflammatory milieu, and tumors underwent rapid regression. Thus, the immunosuppressive milieu in tumors must be actively maintained, and tumors become susceptible to immune attack if the PTEN pathway in Tregs is disrupted.

scholarly journals Targeting AURKA in Cancer: molecular mechanisms and opportunities for Cancer therapy

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Ruijuan Du ◽  
Chuntian Huang ◽  
Kangdong Liu ◽  
Xiang Li ◽  
Zigang Dong
Keyword(s):  
Cancer Therapy ◽  
Molecular Mechanisms ◽  
Aurora Kinase ◽  
Interacting Proteins ◽  
Multiple Tumor ◽  
Oncogenic Pathways ◽  
Wide Range ◽  
The Family ◽  
Tumor Types ◽  
Cancer Tissues

AbstractAurora kinase A (AURKA) belongs to the family of serine/threonine kinases, whose activation is necessary for cell division processes via regulation of mitosis. AURKA shows significantly higher expression in cancer tissues than in normal control tissues for multiple tumor types according to the TCGA database. Activation of AURKA has been demonstrated to play an important role in a wide range of cancers, and numerous AURKA substrates have been identified. AURKA-mediated phosphorylation can regulate the functions of AURKA substrates, some of which are mitosis regulators, tumor suppressors or oncogenes. In addition, enrichment of AURKA-interacting proteins with KEGG pathway and GO analysis have demonstrated that these proteins are involved in classic oncogenic pathways. All of this evidence favors the idea of AURKA as a target for cancer therapy, and some small molecules targeting AURKA have been discovered. These AURKA inhibitors (AKIs) have been tested in preclinical studies, and some of them have been subjected to clinical trials as monotherapies or in combination with classic chemotherapy or other targeted therapies.

scholarly journals Integration of tumor inflammation, cell proliferation, and traditional biomarkers improves prediction of immunotherapy resistance and response

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Sarabjot Pabla ◽  
R. J. Seager ◽  
Erik Van Roey ◽  
Shuang Gao ◽  
Carrie Hoefer ◽  
...  
Keyword(s):  
Immune Response ◽  
Cell Proliferation ◽  
Cell Activation ◽  
Response Rate ◽  
Checkpoint Inhibitors ◽  
Host Immune Response ◽  
Gene Signature ◽  
B Cell Activation ◽  
Rna Seq ◽  
Multiple Tumor

Abstract Background Contemporary to the rapidly evolving landscape of cancer immunotherapy is the equally changing understanding of immune tumor microenvironments (TMEs) which is crucial to the success of these therapies. Their reliance on a robust host immune response necessitates clinical grade measurements of immune TMEs at diagnosis. In this study, we describe a stable tumor immunogenic profile describing immune TMEs in multiple tumor types with ability to predict clinical benefit from immune checkpoint inhibitors (ICIs). Methods A tumor immunogenic signature (TIGS) was derived from targeted RNA-sequencing (RNA-seq) and gene expression analysis of 1323 clinical solid tumor cases spanning 35 histologies using unsupervised analysis. TIGS correlation with ICI response and survival was assessed in a retrospective cohort of NSCLC, melanoma and RCC tumor blocks, alone and combined with TMB, PD-L1 IHC and cell proliferation biomarkers. Results Unsupervised clustering of RNA-seq profiles uncovered a 161 gene signature where T cell and B cell activation, IFNg, chemokine, cytokine and interleukin pathways are over-represented. Mean expression of these genes produced three distinct TIGS score categories: strong (n = 384/1323; 29.02%), moderate (n = 354/1323; 26.76%), and weak (n = 585/1323; 44.22%). Strong TIGS tumors presented an improved ICI response rate of 37% (30/81); with highest response rate advantage occurring in NSCLC (ORR = 36.6%; 16/44; p = 0.051). Similarly, overall survival for strong TIGS tumors trended upward (median = 25 months; p = 0.19). Integrating the TIGS score categories with neoplastic influence quantified via cell proliferation showed highly proliferative and strong TIGS tumors correlate with significantly higher ICI ORR than poorly proliferative and weak TIGS tumors [14.28%; p = 0.0006]. Importantly, we noted that strong TIGS and highly [median = not achieved; p = 0.025] or moderately [median = 16.2 months; p = 0.025] proliferative tumors had significantly better survival compared to weak TIGS, highly proliferative tumors [median = 7.03 months]. Importantly, TIGS discriminates subpopulations of potential ICI responders that were considered negative for response by TMB and PD-L1. Conclusions TIGS is a comprehensive and informative measurement of immune TME that effectively characterizes host immune response to ICIs in multiple tumors. The results indicate that when combined with PD-L1, TMB and cell proliferation, TIGS provides greater context of both immune and neoplastic influences on the TME for implementation into clinical practice.

scholarly journals Cyclin B1 acts as a tumor microenvironment-related cancer promoter and prognostic biomarker in hepatocellular carcinoma

2021 ◽  
Vol 49 (5) ◽  
pp. 030006052110162
Author(s):  
Yangming Hou ◽  
Xin Wang ◽  
Junwei Wang ◽  
Xuemei Sun ◽  
Xinbo Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Tumor Microenvironment ◽  
Proportional Hazards ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Cyclin B1 ◽  
Cox Proportional Hazards ◽  
Gene Set Enrichment Analysis ◽  
Tumor Promoter ◽  
Protein Protein Interaction

Objectives The present study aimed to develop a gene signature based on the ESTIMATE algorithm in hepatocellular carcinoma (HCC) and explore possible cancer promoters. Methods The ESTIMATE and CIBERSORT algorithms were applied to calculate the immune/stromal scores and the proportion of tumor-infiltrating immune cells (TICs) in a cohort of HCC patients. The differentially expressed genes (DEGs) were screened by Cox proportional hazards regression analysis and protein–protein interaction (PPI) network construction. Cyclin B1 (CCNB1) function was verified using experiments. Results The stromal and immune scores were associated with clinicopathological factors and recurrence-free survival (RFS) in HCC patients. In total, 546 DEGs were up-regulated in low score groups, 127 of which were associated with RFS. CCNB1 was regarded as the most predictive factor closely related to prognosis of HCC and could be a cancer promoter. Gene Set Enrichment Analysis (GSEA) and CIBERSORT analyses indicated that CCNB1 levels influenced HCC tumor microenvironment (TME) immune activity. Conclusions The ESTIMATE signature can be used as a prognosis tool in HCC. CCNB1 is a tumor promoter and contributes to TME status conversion.

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