Investigating the tumor immune infiltrate for populations that predict immune-related adverse events (irAEs) in patients receiving PD-1 inhibitors.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 3116-3116
Author(s):  
Steven Michael Blum ◽  
Neal Smith ◽  
Moshe Sade-Feldman ◽  
Dennie T. Frederick ◽  
Russell William Jenkins ◽  
...  
Keyword(s):  
T Cell ◽  
Adverse Events ◽  
Single Cell ◽  
Immune Cells ◽  
Checkpoint Inhibitors ◽  
Advanced Melanoma ◽  
Cell Populations ◽  
Data Set ◽  
Systemic Immunosuppression ◽  
Definition Of

3116 Background: The mechanistic relationship between clinical benefit and immune-related adverse events (irAEs) in response to immune checkpoint inhibitors (ICIs) remains unclear, with several clinical studies reporting that irAEs are biomarkers of responses. Single-cell RNA sequencing (scRNAseq) analysis of tumors from patients with advanced melanoma before and after treatment with ICIs have identified immune cells that correlate with response to ICIs. We sought to evaluate if these populations were also associated with irAEs. Methods: A published scRNAseq data set generated with the Smart-Seq2 protocol (Sade-Feldman M, et al. Cell 2018.) was re-analyzed, stratified by two definitions of irAEs: (1) toxicity requiring systemic immunosuppression (prednisone > 10mg/day) or (2) systemic immunosuppression and/or endocrinopathy. Unbiased single-cell analysis was performed, followed by sub-clustering of T cell populations. The percentage of cells in each cluster was determined on a per sample basis. Results: 13,184 immune cells from 39 samples collected from 25 patients were re-analyzed. 27 samples were from patients who did not respond to ICIs, while 12 samples came from responding patients. 21 samples came from patients who required immunosuppression, 5 samples from patients with isolated thyroiditis, and 13 samples from patients who met neither irAE criteria. Unsupervised scRNAseq analyses focused on ICI efficacy re-capitulated published associations between response and populations that included B-cells (p < 0.01) and TCF7 expressing T-cells (p < 0.01). While these cell populations were not associated with either definition of toxicity, we observed a non-Treg CD4 expressing T cell population (0.8-10.5% cells/sample) that positively correlated with either definition of toxicity (p < 0.05) but not efficacy. Conclusions: In a patient cohort with advanced melanoma, tumor-infiltrating immune cell populations associated with response to ICI therapy were not associated with irAEs. This suggests that biomarkers of ICI response may not function as biomarkers of irAEs, and ongoing analysis will seek to validate this result. Understanding the differences between ICI response and irAEs may identify new therapeutic targets for maximizing efficacy while mitigating toxicity.

scholarly journals From Oncogenic Signaling Pathways to Single-Cell Sequencing of Immune Cells: Changing the Landscape of Cancer Immunotherapy

Molecules ◽  
2021 ◽  
Vol 26 (8) ◽  
pp. 2278
Author(s):  
Afshin Derakhshani ◽  
Zeinab Rostami ◽  
Hossein Safarpour ◽  
Mahdi Abdoli Shadbad ◽  
Niloufar Sadat Nourbakhsh ◽  
...  
Keyword(s):  
Single Cell ◽  
Signaling Pathways ◽  
Immune Cells ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Cancer Development ◽  
Immune Checkpoints ◽  
Single Cell Sequencing ◽  
Increased Risk

Over the past decade, there have been remarkable advances in understanding the signaling pathways involved in cancer development. It is well-established that cancer is caused by the dysregulation of cellular pathways involved in proliferation, cell cycle, apoptosis, cell metabolism, migration, cell polarity, and differentiation. Besides, growing evidence indicates that extracellular matrix signaling, cell surface proteoglycans, and angiogenesis can contribute to cancer development. Given the genetic instability and vast intra-tumoral heterogeneity revealed by the single-cell sequencing of tumoral cells, the current approaches cannot eliminate the mutating cancer cells. Besides, the polyclonal expansion of tumor-infiltrated lymphocytes in response to tumoral neoantigens cannot elicit anti-tumoral immune responses due to the immunosuppressive tumor microenvironment. Nevertheless, the data from the single-cell sequencing of immune cells can provide valuable insights regarding the expression of inhibitory immune checkpoints/related signaling factors in immune cells, which can be used to select immune checkpoint inhibitors and adjust their dosage. Indeed, the integration of the data obtained from the single-cell sequencing of immune cells with immune checkpoint inhibitors can increase the response rate of immune checkpoint inhibitors, decrease the immune-related adverse events, and facilitate tumoral cell elimination. This study aims to review key pathways involved in tumor development and shed light on single-cell sequencing. It also intends to address the shortcomings of immune checkpoint inhibitors, i.e., their varied response rates among cancer patients and increased risk of autoimmunity development, via applying the data from the single-cell sequencing of immune cells.

scholarly journals Mapping the immune environment in clear cell renal carcinoma by single-cell genomics

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Nicholas Borcherding ◽  
Ajaykumar Vishwakarma ◽  
Andrew P. Voigt ◽  
Andrew Bellizzi ◽  
Jacob Kaplan ◽  
...  
Keyword(s):  
T Cell ◽  
Single Cell ◽  
Immune Cells ◽  
Clear Cell ◽  
Cell Receptor ◽  
Renal Tissue ◽  
High Response Rate ◽  
Cell Renal Cell Carcinoma ◽  
Tumor Mutational Burden ◽  
Macrophage Populations

AbstractClear cell renal cell carcinoma (ccRCC) is one of the most immunologically distinct tumor types due to high response rate to immunotherapies, despite low tumor mutational burden. To characterize the tumor immune microenvironment of ccRCC, we applied single-cell-RNA sequencing (SCRS) along with T-cell-receptor (TCR) sequencing to map the transcriptomic heterogeneity of 25,688 individual CD45+ lymphoid and myeloid cells in matched tumor and blood from three patients with ccRCC. We also included 11,367 immune cells from four other individuals derived from the kidney and peripheral blood to facilitate the identification and assessment of ccRCC-specific differences. There is an overall increase in CD8+ T-cell and macrophage populations in tumor-infiltrated immune cells compared to normal renal tissue. We further demonstrate the divergent cell transcriptional states for tumor-infiltrating CD8+ T cells and identify a MKI67 + proliferative subpopulation being a potential culprit for the progression of ccRCC. Using the SCRS gene expression, we found preferential prediction of clinical outcomes and pathological diseases by subcluster assignment. With further characterization and functional validation, our findings may reveal certain subpopulations of immune cells amenable to therapeutic intervention.

Single Cell Profiling Reveals Unique CXCL13 Positive T Cell Subsets in the Tumor Microenvironment of Lymphocyte Rich Classic Hodgkin Lymphoma

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 32-33
Author(s):  
Tomohiro Aoki ◽  
Lauren C. Chong ◽  
Katsuyoshi Takata ◽  
Katy Milne ◽  
Elizabeth Chavez ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
B Cells ◽  
Single Cell ◽  
Immune Cells ◽  
Research Funding ◽  
Immune Cell ◽  
Expression Patterns ◽  
The Other ◽  
Tfh Cells

Introduction: Classic Hodgkin lymphoma (CHL) features a unique crosstalk between malignant cells and different types of normal immune cells in the tumor-microenvironment (TME). On the basis of histomorphologic and immunophenotypic features of the malignant Hodgkin and Reed-Sternberg (HRS) cells and infiltrating immune cells, four histological subtypes of CHL are recognized: Nodular sclerosing (NS), Mixed cellularity, Lymphocyte-rich (LR) and Lymphocyte-depleted CHL. Recently, our group described the high abundance of various types of immunosuppressive CD4+ T cells including LAG3+ and/or CTLA4+ cells in the TME of CHL using single cell RNA sequencing (scRNAseq). However, the TME of LR-CHL has not been well characterized due to the rarity of the disease. In this study, we aimed at characterizing the immune cell profile of LR-CHL at single cell resolution. METHODS: We performed scRNAseq on cell suspensions collected from lymph nodes of 28 primary CHL patients, including 11 NS, 9 MC and 8 LR samples, with 5 reactive lymph nodes (RLN) serving as normal controls. We merged the expression data from all cells (CHL and RLN) and performed batch correction and normalization. We also performed single- and multi-color immunohistochemistry (IHC) on tissue microarray (TMA) slides from the same patients. In addition, an independent validation cohort of 31 pre-treatment LR-CHL samples assembled on a TMA, were also evaluated by IHC. Results: A total of 23 phenotypic cell clusters were identified using unsupervised clustering (PhenoGraph). We assigned each cluster to a cell type based on the expression of genes described in published transcriptome data of sorted immune cells and known canonical markers. While most immune cell phenotypes were present in all pathological subtypes, we observed a lower abundance of regulatory T cells (Tregs) in LR-CHL in comparison to the other CHL subtypes. Conversely, we found that B cells were enriched in LR-CHL when compared to the other subtypes and specifically, all four naïve B-cell clusters were quantitatively dominated by cells derived from the LR-CHL samples. T follicular helper (TFH) cells support antibody response and differentiation of B cells. Our data show the preferential enrichment of TFH in LR-CHL as compared to other CHL subtypes, but TFH cells were still less frequent compared to RLN. Of note, Chemokine C-X-C motif ligand 13 (CXCL13) was identified as the most up-regulated gene in LR compared to RLN. CXCL13, which is a ligand of C-X-C motif receptor 5 (CXCR5) is well known as a B-cell attractant via the CXCR5-CXCL13 axis. Analyzing co-expression patterns on the single cell level revealed that the majority of CXCL13+ T cells co-expressed PD-1 and ICOS, which is known as a universal TFH marker, but co-expression of CXCR5, another common TFH marker, was variable. Notably, classical TFH cells co-expressing CXCR5 and PD-1 were significantly enriched in RLN, whereas PD-1+ CXCL13+ CXCR5- CD4+ T cells were significantly enriched in LR-CHL. These co-expression patterns were validated using flow cytometry. Moreover, the expression of CXCR5 on naïve B cells in the TME was increased in LR-CHL compared to the other CHL subtypes We next sought to understand the spatial relationship between CXCL13+ T cells and malignant HRS cells. IHC of all cases revealed that CXCL13+ T cells were significantly enriched in the LR-CHL TME compared to other subtypes of CHL, and 46% of the LR-CHL cases showed CXCL13+ T cell rosettes closely surrounding HRS cells. Since PD-1+ T cell rosettes are known as a specific feature of LR-CHL, we confirmed co-expression of PD-1 in the rosetting cells by IHC in these cases. Conclusions: Our results reveal a unique TME composition in LR-CHL. LR-CHL seems to be distinctly characterized among the CHL subtypes by enrichment of CXCR5+ naïve B cells and CD4+ CXCL13+ PD-1+ T cells, indicating the importance of the CXCR5-CXCL13 axis in the pathogenesis of LR-CHL. Figure Disclosures Savage: BeiGene: Other: Steering Committee; Merck, BMS, Seattle Genetics, Gilead, AstraZeneca, AbbVie: Honoraria; Roche (institutional): Research Funding; Merck, BMS, Seattle Genetics, Gilead, AstraZeneca, AbbVie, Servier: Consultancy. Scott:Janssen: Consultancy, Research Funding; Celgene: Consultancy; NanoString: Patents & Royalties: Named inventor on a patent licensed to NanoString, Research Funding; NIH: Consultancy, Other: Co-inventor on a patent related to the MCL35 assay filed at the National Institutes of Health, United States of America.; Roche/Genentech: Research Funding; Abbvie: Consultancy; AstraZeneca: Consultancy. Steidl:AbbVie: Consultancy; Roche: Consultancy; Curis Inc: Consultancy; Juno Therapeutics: Consultancy; Bayer: Consultancy; Seattle Genetics: Consultancy; Bristol-Myers Squibb: Research Funding.

scholarly journals Single-Cell Analysis Reveals Characterization of Infiltrating T Cells in Moderately Differentiated Colorectal Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Xi Yang ◽  
Quan Qi ◽  
Yuefen Pan ◽  
Qing Zhou ◽  
Yinhang Wu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Rectal Cancer ◽  
Colon Cancer ◽  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Peripheral Blood ◽  
Strong Correlation ◽  
Tumor Tissue ◽  
Cell Populations

ObjectiveThis study aimed to characterize the tumor-infiltrating T cells in moderately differentiated colorectal cancer.MethodsUsing single-cell RNA sequencing data of isolated 1632 T cells from tumor tissue and 1252 T cells from the peripheral blood of CRC patients, unsupervised clustering analysis was performed to identify functionally distinct T cell populations, followed by correlations and ligand-receptor interactions across cell types. Finally, differential analysis of the tumor-infiltrating T cells between colon cancer and rectal cancer were carried out.ResultsA total of eight distinct T cell populations were identified from tumor tissue. Tumor-Treg showed a strong correlation with Th17 cells. CD8+TRM was positively correlated with CD8+IEL. Seven distinct T cell populations were identified from peripheral blood. There was a strong correlation between CD4+TN and CD4+blood-TCM. Colon cancer and rectal cancer showed differences in the composition of tumor-infiltrating T cell populations. Tumor-infiltrating CD8+IEL cells were found in rectal cancer but not in colon cancer, while CD8+ TN cells were found in the peripheral blood of colon cancer but not in that of rectal cancer. A larger number of tumor-infiltrating CD8+ Tex (88.94%) cells were found in the colon cancer than in the rectal cancer (11.06%). The T cells of the colon and rectal cancers showed changes in gene expression pattern.ConclusionsWe characterized the T cell populations in the CRC tumor tissue and peripheral blood.

Comprehensive ambulatory monitoring during immunotherapy in patients with advanced melanoma: A prospective trial (CAMP-IT).

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. TPS1589-TPS1589
Author(s):  
Milan Kos ◽  
Laurien Buffart ◽  
Jan Willem de Groot ◽  
Hans Westgeest ◽  
Wouter Dercksen ◽  
...  
Keyword(s):  
Adverse Events ◽  
Real Time ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Ambulatory Monitoring ◽  
Vital Signs ◽  
Advanced Melanoma ◽  
Smartphone App ◽  
Survival Outcomes ◽  
Activity Data

TPS1589 Background: The emergence of immune checkpoint inhibitors has improved survival outcomes for patients with advanced melanoma. However, these treatment modalities are also associated with specific immune-related toxicities. These are often reversible after prompt recognition and initiation of appropriate management, but can result in severe morbidity and hamper health-related quality of life (HRQoL) if left undetected. Hence, accurate and regular monitoring of these patients is critical. Recent advances in mHealth technologies and the rapidly expanding armamentarium of wearable devices allow for real-time objective (vital signs and physical activity) data and patient-reported outcome measurement (PROMs) collection and, hence, serve this purpose. We hypothesize that collection of real-time objective data adds to the early detection of disease- and treatment-related adverse events. The primary objective of this study is to determine the feasibility of collecting real-time PROMs, vital signs, and physical activity data in advanced melanoma patients receiving immunotherapy using a comprehensive ambulatory monitoring platform (CAMP) that consists of a smartphone app, activity monitor, digital thermometer, and online dashboard for physicians. Methods: In this prospective multi-center trial, patients (n = 50) with advanced melanoma, scheduled to receive immunotherapy with immune checkpoint inhibitors, and with access to a smartphone are eligible for inclusion. Consenting patients will be asked to wear a FitBit Versa 2.0 during waking hours, collect daily temperature measurements using a Withings Smart Temporal thermometer, and answer weekly toxicity questionnaires (NCI PRO-CTCAE) using the smartphone app for the duration of the study (12 weeks). Primary outcome is feasibility in terms of (i) participation rates, (ii) wear-time, (iii) compliance rates with in-app questionnaires and temperature measurements, and (iv) satisfaction with the platform. Secondary exploratory outcomes include associations between CAMP-derived parameters and clinical outcomes: performance status (PS), HR-QoL scores (EORTC QLQ-C30 questionnaire), unplanned hospitalizations, physician-assessed adverse events, and 1-year survival outcomes. PS and HR-QoL will be rated at baseline, mid-study, and end-of-study. The occurrence of adverse events will be documented up to 12 months from baseline. Survival outcomes will be compared to a propensity score matched group from the Netherlands Cancer Registry. Accrual has started in February 2021. Clinical trial information: NL8827.

scholarly journals Immune Checkpoint Inhibitors and Immune-Related Adverse Events in Patients With Advanced Melanoma

2020 ◽  
Vol 3 (3) ◽  
pp. e201611 ◽  
Author(s):  
Ching-Yuan Chang ◽  
Haesuk Park ◽  
Daniel C. Malone ◽  
Ching-Yu Wang ◽  
Debbie L. Wilson ◽  
...  
Keyword(s):  
Adverse Events ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Advanced Melanoma

scholarly journals Mechanisms of immune-related adverse events during the treatment of cancer with immune checkpoint inhibitors

Rheumatology ◽  
2019 ◽  
Vol 58 (Supplement_7) ◽  
pp. vii59-vii67 ◽  
Author(s):  
Sophia C Weinmann ◽  
David S Pisetsky
Keyword(s):  
T Cell ◽  
Adverse Events ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Immunosuppressive Treatment ◽  
Cell Repertoire ◽  
Autoantibody Production ◽  
Rheumatic Manifestations ◽  
Checkpoint Inhibitor Therapy

AbstractImmune checkpoint inhibitors are novel biologic agents to treat cancer by inhibiting the regulatory interactions that limit T cell cytotoxicity to tumours. Current agents target either CTLA-4 or the PD-1/PD-L1 axis. Because checkpoints may also regulate autoreactivity, immune checkpoint inhibitor therapy is complicated by side effects known as immune-related adverse events (irAEs). The aim of this article is to review the mechanisms of these events. irAEs can involve different tissues and include arthritis and other rheumatic manifestations. The frequency of irAEs is related to the checkpoint inhibited, with the combination of agents more toxic. Because of their severity, irAEs can limit therapy and require immunosuppressive treatment. The mechanisms leading to irAEs are likely similar to those promoting anti-tumour responses and involve expansion of the T cell repertoire; furthermore, immune checkpoint inhibitors can affect B cell responses and induce autoantibody production. Better understanding of the mechanisms of irAEs will be important to improve patient outcome as well as quality of life during treatment.

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