Identification of Sepsis Patient Immune Phenotypes Using a Microfluidic Assay

1006 Background: IMpassion130 was the first randomized phase 3 study to show clinical benefit of cancer immunotherapy (CIT) in untreated PD-L1+ mTNBC. Enhanced A + nP efficacy vs placebo (P) + nP was seen in pts with a richer immune TME but was confined to PD-L1 IC+ pts (PD-L1–expressing immune cells on ≥1% of tumor area; Emens JNCI 2021). While TNBC molecular subtyping and CD8 localization are prognostic in early TNBC, it is unknown whether these features are associated with CIT benefit in mTNBC. This exploratory analysis aimed to identify TME components associated with A + nP efficacy in IMpassion130. Methods: IHC was used to assess PD-L1 status (VENTANA SP142) and immune phenotypes (inflamed/excluded/desert per CD8 stromal/intratumoral localization; Mariathasan Nature 2018). RNA-seq was used for molecular subtyping (Burstein CCR 2015) and pathway analyses (MSigDB Hallmark). Cox regression was used to compare PFS/OS between A + nP vs P + nP, adjusted for prior taxanes, liver mets. Results: Sample classification and PD-L1 distribution are shown (Table). Improved PFS with A + nP vs P + nP was seen in PD-L1 IC+ inflamed and excluded tumors, but improved OS was limited to PD-L1 IC+ inflamed tumors. PD-L1 IC+ basal-like immune activated (BLIA) and immune suppressed (BLIS) subgroups derived PFS benefit, but OS benefit was limited to PD-L1 IC+ BLIA subgroups. In PD-L1 IC+ pts, pathway analysis identified proliferation/DNA damage repair (basal-like tumor features) and angiogenesis/ER response (higher in luminal androgen receptor [LAR]/ mesenchymal [MES] tumors) were associated with improved and reduced PFS, respectively. Conclusions: PD-L1 IC+ immune-inflamed tumors and PD-L1 IC+ BLIA tumors show highest CIT sensitivity, and LAR tumors may be resistant to CIT. These data warrant further study and validation. Clinical trial information: NCT02425891 .[Table: see text]

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Investigation of Genetic Determinants of Glioma Immune Phenotype by Integrative Immunogenomic Scale Analysis

Frontiers in Immunology ◽

10.3389/fimmu.2021.557994 ◽

2021 ◽

Vol 12 ◽

Author(s):

Binghao Zhao ◽

Yuekun Wang ◽

Yaning Wang ◽

Congxin Dai ◽

Yu Wang ◽

...

Keyword(s):

Immune Cell ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Clinicopathological Characteristics ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Immune Phenotype ◽

Immune Phenotypes ◽

Glioma Microenvironment ◽

Genome Atlas

The immunosuppressive mechanisms of the surrounding microenvironment and distinct immunogenomic features in glioblastoma (GBM) have not been elucidated to date. To fill this gap, useful data were extracted from The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), GSE16011, GSE43378, GSE23806, and GSE12907. With the ssGSEA method and the ESTIMATE and CIBERSORT algorithms, four microenvironmental signatures were used to identify glioma microenvironment genes, and the samples were reasonably classified into three immune phenotypes. The molecular and clinical features of these phenotypes were characterized via key gene set expression, tumor mutation burden, fraction of immune cell infiltration, and functional enrichment. Exhausted CD8+ T cell (GET) signature construction with the predictive response to commonly used antitumor drugs and peritumoral edema assisted in further characterizing the immune phenotype features. A total of 2,466 glioma samples with gene expression profiles were enrolled. Tumor purity, ESTIMATE, and immune and stromal scores served as the 4 microenvironment signatures used to classify gliomas into immune-high, immune-middle and immune-low groups, which had distinct immune heterogeneity and clinicopathological characteristics. The immune-H phenotype had higher expression of four immune signatures; however, most checkpoint molecules exhibited poor survival. Enriched pathways among the subtypes were related to immunity. The GET score was similar among the three phenotypes, while immune-L was more sensitive to bortezomib, cisplatin, docetaxel, lapatinib, and rapamycin prescriptions and displayed mild peritumor edema. The three novel immune phenotypes with distinct immunogenetic features could have utility for understanding glioma microenvironment regulation and determining prognosis. These results contribute to classifying glioma subtypes, remodeling the immunosuppressive microenvironment and informing novel cancer immunotherapy in the era of precision immuno-oncology.

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P02.03 Automated cell type specific PD-L1 quantification by artificial intelligence using high throughput bleach & stain 15-marker multiplex fluorescence immunohistochemistry in human cancers

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2021-itoc8.15 ◽

2021 ◽

Vol 9 (Suppl 1) ◽

pp. A8.2-A9

Author(s):

NC Blessin ◽

E Bady ◽

T Mandelkow ◽

C Yang ◽

J Raedler ◽

...

Keyword(s):

Breast Cancer ◽

Tumor Cells ◽

Immune Cells ◽

T Cell Subsets ◽

Immune Microenvironment ◽

L1 Data ◽

Tumor Immune Microenvironment ◽

Immune Phenotypes ◽

Fluorescence Immunohistochemistry ◽

Tumor Entities

BackgroundThe quantification of PD-L1 (programmed cell death ligand 1) has been used to predict patient’s survival, to characterize the tumor immune microenvironment, and to predict response to immune checkpoint therapies. However, a framework to assess the PD-L1 status with a high interobserver reproducibility on tumor cells and different types of immune cells has yet to be established.Materials and MethodsTo study the impact of PD-L1 expression on the tumor immune microenvironment and patient outcome, a framework for fully automated PD-L1 quantification on tumor cells and immune cells was established and validated. Automated PD-L1 quantification was facilitated by incorporating three different deep learning steps for the analysis of more than 80 different neoplasms from more than 10’000 tumor specimens using a bleach & stain 15-marker multiplex fluorescence immunohistochemistry panel (i.e., PD-L1, PD-1, CTLA-4, panCK, CD68, CD163, CD11c, iNOS, CD3, CD8, CD4, FOXP3, CD20, Ki67, CD31). Clinicopathological parameter were available for more than 30 tumor entities and overall survival data were available for 1517 breast cancer specimens.ResultsComparing the automated deep-learning based PD-L1 quantification with conventional brightfield PD-L1 data revealed a high concordance in tumor cells (p<0.0001) as well as immune cells (p<0.0001) and an accuracy of the automated PD-L1 quantification ranging from 90% to 95.2%. Across all tumor entities, the PD-L1 expression level was significantly higher in distinct macrophage/dendritic cell (DC) subsets (identified by CD68, CD163, CD11c, iNOS; p<000.1) and in macrophages/DCs located in the Stroma (p<0.0001) as compared to intratumoral macrophages/DC subsets. Across all different tumor entities, the PD-L1 expression was highly variable and distinct PD-L1 driven immune phenotypes were identified based on the PD-L1 intensity on both tumor and immune cells, the distance between non-exhausted T-cell subsets (i.e. PD-1 and CTLA-4 expression on CD3+CD8+ cytotoxic T-cells, CD3+CD4+ T-helper cells, CD3+CD4+FOXP3+ regulatory T-cells) and tumor cells as well as macrophage/(DC) subtypes. In breast cancer, the PD-L1 fluorescence intensity on tumor cells showed a significantly higher predictive performance for overall survival with an area under receiver operating curves (AUC) of 0.72 (p<0.0001) than the percentage of PD-L1+ tumor cells (AUC: 0.54). In PD-L1 positive as well as negative breast cancers a close spatial relationship between T- cell subsets (CD3+CD4±CD8±FOXP3±PD-1±CTLA-4±) and Macrophage/DC subsets (CD68±CD163±CD11c±iNOS) was found prognostic relevant (p<0.0001).ConclusionsIn conclusion, multiplex immunofluorescence PD-L1 assessment provides cutoff-free/continuous PD-L1 data which are superior to the conventional percentage of PD-L1+ tumor cells and of high prognostic relevance. The combined analysis of spatial PD-L1/PD-1 data and more than 20 different immune cell subtypes of the immune tumor microenvironment revealed distinct PD-L1 immune phenotypes.Disclosure InformationN.C. Blessin: None. E. Bady: None. T. Mandelkow: None. C. Yang: None. J. Raedler: None. R. Simon: None. C. Fraune: None. M. Lennartz: None. S. Minner: None. E. Burandt: None. D. Höflmayer: None. G. Sauter: None. S.A. Weidemann: None.

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921 Comparison of PI3K/AKT/mTOR pathway profiles amongst three immune phenotypes classified by artificial intelligence-powered H&E analyzer in non-small cell lung cancer

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2021-sitc2021.921 ◽

2021 ◽

Vol 9 (Suppl 3) ◽

pp. A966-A966

Author(s):

Hyung-Gyo Cho ◽

Grace Lee ◽

Hye Sung Kim ◽

Sanghoon Song ◽

Kyunghyun Paeng ◽

...

Keyword(s):

Lung Cancer ◽

Tumor Microenvironment ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Small Cell ◽

Mtor Pathway ◽

Small Cell Lung ◽

Gene Set ◽

Mtorc1 Signaling ◽

Immune Phenotypes

BackgroundThe phosphatidylinositol 3-kinase (PI3K)/Akt/mechanistic target of rapamycin (mTOR) pathway plays a significant role in both tumorigenesis and progression of disease in non-small cell lung cancer (NSCLC).1 Increased activation of the pathway, whether in tumor or immune cells, results in an immunosuppressive tumor microenvironment.2 Therefore, we looked into how this pathway differs in three distinct NSCLC immune phenotypes.MethodsLunit SCOPE IO (Lunit, Seoul, Republic of Korea), a deep learning-based hematoxylin and eosin (H&E) image analytics tool, identifies lymphocytes and quantifies lymphocyte density within the cancer epithelium (CE-Lym), stroma (CS-Lym), and combined area (C-Lym). We applied Lunit-SCOPE IO to H&E-stained tissue images of 965 NSCLC samples from The Cancer Genome Atlas (TCGA). Tumors in the lowest tertile of C-Lym were labeled as immune-desert, and the remaining tumors were classified as inflamed and immune-excluded according to the median of the ratio of CE-Lym to CS-Lym.Utilizing RNA-sequencing data from TCGA, gene set enrichment analysis (GSEA) was conducted to analyze the differences in mTORC1 and PI3K/Akt/mTOR signaling between the subtypes.3 We obtained mutational data related to the PI3K/Akt/mTOR pathway from cBioPortal to compare the ratio of functional mutations between the immune phenotypes.4ResultsThe mTORC1 signaling gene set was consistently enriched in immune-excluded, whether compared to inflamed (padj < 0.01, normalized enrichment score [NES]: 2.3) or immune-desert (padj < 0.01, NES: 1.6). However, PI3K/Akt/mTOR signaling gene set enrichment did not show statistically significant differences between the immune phenotypes.Within the three immune phenotypes, we analyzed three functional mutations: PIK3CA, PTEN, and Akt1 (figure 1). Of the total 112 samples showing the functional mutations of the PI3K/Akt/mTOR pathway, 53 were immune-excluded, 31 inflamed, and 28 immune-desert. The relation between mutation frequency and the immune subtypes was significant (X2 (2) = 11.1979, p < .01). The immune-excluded was more likely than the other subtypes to have functional PI3K/Akt/mTOR mutations.Abstract 921 Figure 1The landscape of functional mutation and immune phenotypes regarding PI3K/Akt/mTOR pathwayConclusionsThe three tissue phenomic subtypes showed different PI3K/Akt/mTOR pathway profiles, with immune-excluded having the most mutation samples and the greatest enhancement of mTORC1 signaling gene set. Likewise, tissue H&E based tumor microenvironment classification by Lunit SCOPE IO can be applied to other hallmark pathways and tumor types, and such further investigation of the tumor microenvironment can provide insights into novel therapeutic avenues.ReferencesTan AC. Targeting the PI3K/Akt/mTOR pathway in non-small cell lung cancer (NSCLC). Thorac Cancer 2020;11(3):511–8.O’Donnell JS, Massi D, Teng MWL, Mandala M. PI3K-AKT-mTOR inhibition in cancer immunotherapy, redux. Semin Cancer Biol 2018;48:91–103.Liberzon A, Birger C, Thorvaldsdóttir H, Ghandi M, Mesirov JP, Tamayo P. The molecular signatures database hallmark gene set collection. Cell Systems 2015;1(6):417–25.Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2012;2(5):401–4.

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CRISPR-based functional genomics in human dendritic cells

10.1101/2020.12.22.423985 ◽

2020 ◽

Author(s):

Marco Jost ◽

Amy N. Jacobson ◽

Jeffrey A. Hussmann ◽

Giana Cirolia ◽

Michael A. Fischbach ◽

...

Keyword(s):

Dendritic Cells ◽

Immune Responses ◽

Genome Editing ◽

Individual Variation ◽

Human Microbiome ◽

Human Monocyte ◽

Genetic Screens ◽

Mucosal Surfaces ◽

Immune Phenotypes ◽

Human Dendritic Cells

AbstractDendritic cells (DCs) regulate processes ranging from antitumor and antiviral immunity to host-microbe communication at mucosal surfaces. It remains difficult, however, to genetically manipulate human DCs, limiting our ability to probe how DCs elicit specific immune responses. Here, we develop a CRISPR/Cas9 genome editing method for human monocyte-derived DCs (moDCs) that mediates knockouts with a median efficiency of >93% across >300 genes. Using this method, we perform genetic screens in moDCs, identifying mechanisms by which DCs tune responses to lipopolysaccharides from the human microbiome. In addition, we reveal donor-specific responses to lipopolysaccharides, underscoring the importance of assessing immune phenotypes in donor-derived cells, and identify genes that control this specificity, highlighting the potential of our method to pinpoint determinants of inter-individual variation in immune responses. Our work sets the stage for a systematic dissection of the immune signaling at the host-microbiome interface and for targeted engineering of DCs for neoantigen vaccination.

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Unique serum immune phenotypes stratify Oklahoma Native American rheumatic disease patients

Arthritis Care & Research ◽

10.1002/acr.24795 ◽

2021 ◽

Author(s):

Samantha Slight‐Webb ◽

Carla J. Guthridge ◽

Joseph Kheir ◽

Hua Chen ◽

Ly Tran ◽

...

Keyword(s):

Native American ◽

Rheumatic Disease ◽

Immune Phenotypes

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Autophagy regulation patterns characterization identifies immune phenotypes and immunotherapy response in head and neck squamous cell carcinoma (HNSCC)

10.21203/rs.3.rs-91606/v1 ◽

2020 ◽

Author(s):

Bo Ma ◽

Hui Li ◽

Mingzhu Zheng ◽

Rui Cao ◽

Riyue Yu

Keyword(s):

Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Head And Neck ◽

Squamous Cell ◽

Scoring System ◽

Clustering Algorithm ◽

Neck Squamous Cell Carcinoma ◽

Integrated Analysis ◽

Molecular Characteristics ◽

Immune Phenotypes

Abstract BackgroundAutophagy degraded and recycled cytoplasmic components to maintain cellular homeostasis under stress conditions, which was recognized as double-edged sword in oncogenesis and novel target in cancer treatment. However, comprehensive analysis of the relationship between autophagy regulation and immunity has not been reported yet. MethodsUnsupervised consensus clustering algorithm was used to identify autophagy regulation patterns. LASSO cox regression algorithm was used to build a scoring system (ATGscore) to represent the individual autophagy regulation pattern. Then integrated analysis of autophagy regulation patterns and ATGscore was performed.ResultsWe have successful depicted five autophagy regulation patterns and established a scoring system (ATGscore) to represent it, which was shown to be significantly correlated with TIME infiltration, immune phenotypes, molecular subtypes, and genetic variation, etc. in 1165 head and neck squamous cell carcinoma (HNSCC) patients. Moreover, ATGscore was an independent prognostic factor and potent predictor for clinical response to immune-checkpoint inhibitors (ICIs) targeting immunotherapy. ConclusionUnderstanding the molecular characteristics of autophagy regulation patterns in HNSCC could help us to depict the underlying mechanism of tumour immunity and lay a solid foundation on combination of autophagy targeting therapies and immunotherapies for clinical application in HNSCC.

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