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Publications

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Publications from a variety of academic and industry groups, including work co-authored by Teiko scientists, demonstrate the critical role that peripheral blood plays in the treatment of cancer

Cancer | Published by Teiko

Mass cytometry immune profiling to detect signatures of clinical activity of bintrafusp alfa in patients with advanced non-small cell lung cancer

McAdams & Medina et al. ASCO (2023)

In this abstract published as part of ASCO 2023, McAdams & Medina et al. present data on immune signatures of clinical activity in patients with advanced NSCLC treated with bintrafusp alfa, a bifunctional anti-PDL1 / TGF-beta trap.

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Cancer | Published by Teiko

Immune features associated with clinical benefit and adverse events in response to immunotherapy in UC patients

Healy & Sweere et al. Cancer Res (2023)

Teiko scientists Drs. Healy & Sweere presented at AACR 2023 on immune features of immune-related adverse events and anti-PD1 response in blood in patients with urothelial carcinoma. These data were generated through a collaboration between the Parker Institute for Cancer Immunotherapy and Teiko Bio.

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Cancer | Published by Teiko

Peripheral immune features associated with severe adverse events in melanoma

Healy & Kovacsovics-Bankowski et al. SITC (2022)

Drs. Healy (Teiko Bio) & Kovacsovics-Bankowski (Huntsman Cancer Institute) presented at SITC 2022 on immune signatures of severe immune-related adverse events in blood in patients with advanced melanoma treated with immunotherapy. This data was generated through a collaboration between Dr. Siwen Hu-Lieskovan of the Huntsman Cancer Institute and Teiko Bio.

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COVID-19 | Teiko co-authors

Mass cytometry reveals a conserved immune trajectory of recovery in hospitalized COVID-19 patients

Burnett, Okholm, & Tenvooren, et al. Immunity (2022)

Burnett, Okholm, & Tenvooren, et al. use immune profiling by mass cytometry to identify an immune trajectory associated with COVID recovery and immune subsets associated with a requirement for mechanical ventilation

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Cancer | Teiko co-authors

Single-cell analysis by mass cytometry reveals metabolic states of early-activated CD8+ T cells during the primary immune response

Levine & Hiam-Galvez et al. Immunity. (2021)

Levine & Hiam-Galvez et al. use mass cytometry to profile metabolic states of early-activated CD8 T cells during infection and non-Hodkins lymphoma.

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Breast | Teiko co-authors

Systemic dysfunction and plasticity of the immune macroenvironment in cancer models

Allen & Hiam et al. Nature Medicine. (2020)

Allen & Hiam et al. use mass cytometry to define changes in immune cell frequencies and states across five tissue types in eight different mouse tumor models.

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Infographic hierarchy with an outline of a human at the top level. The second level has two groups, Peripheral Blood and Tumor Biopsy. The third level has different types of immune cells: T cells, B cells, NK cells, Monocytes, and Granulocytes. The fourth level has all of the subsets within each cell type listed as text. There are roughly 9 T cell subsets, 4 B cell subsets, 2 NK subsets, 8 Monocyte (and macrophage) subsets, and 4 Granulocyte subsets.

GvHD | Teiko co-authors

Comprehensive immune monitoring of clinical trials to advance human immunotherapy

Hartmann et al. Cell Rep (2019)

Hartmann et al. describes the development and utilization of a comprehensive and easily customizable 33-marker mass cytometry panel optimized for immune monitoring of clinical trials.

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Breast | Teiko co-authors

Systemic immunity Is required for effective cancer immunotherapy

Spitzer, Carmi, & Reticker-Flynn et al. Cell (2017)

In this seminal Cell 2017 publication, Spitzer, Carmi & Reticker-Flynn et al. show that effective immunotherapy treatment requires peripheral immune cell engagement in both breast and colon cancer mouse models.

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Graphical abstract. The top half is labeled "Baseline" and the bottom half is labeled "anti-PD-L1 ICB". On top there is a lymph node labeled "uninvolved regional lymph node" with 2 dendritic cells, 3 pairs of Tpex cells, 2 Tex-int cells, and 1 Treg. The bottom (anti-PD-L1 ICB) is split in half with each side containing a lymph node and blood vessel and a vertical dotted line separating the two. The lymph node on the left looks similar to above, but with more Tex-int cells and these all have a yellow dot representing KI-67 within them. The blood streaam also has three of these KI-67+ Tex-int cells. On the right this lymph node is labeled "metastatic" and has a big mass of cancer cells blocking most of the lymph node. What remains is a single cell of each type (DC, Tpex, Tex-int, and Treg) and the Tex-int cell does not have KI-67. In the blood vessel is a single Tex-int cell without KI-67.

Cancer | Teiko co-authors

Dynamic CD8+ T cell responses to cancer immunotherapy in human regional lymph nodes are disrupted in metastatic lymph nodes

Rahim, Okholm, & Jones et al. Cell (2023)

Rahim, Okholm, & Jones et al. show that immune checkpoint blockade activates CD8 T cell differentiation and proliferation in the lymph node, not within the tumor microenvironment, and these cells can be detected in transit via blood profiling.

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$Illustrated infographic. At the top in center is a gut with two locations circled, one in gray another in red. An arrow to the right points to an antibody with the text “anti-TNF 70% efficacy”. An arrow to the left points to the words “single-cell sequencing” and a flow chart. Following single-cell sequencing there’s a box labeled “G.I.M.A.T.S. score” with four icons representing: IgG plasma cells (G), inflammatory mononuclear phagocytes (I.M.), activated T cells (A.T.), and stromal cells (S). An illustrated dot plot next to it shows scRNAseq on the x-axis and mass cytometry on the y-axis. The dots appear to fit along an x=y line to suggest strong correlation. At the bottom an illustrated line graph with “GIMATS score” on the x-axis and “cumulative patient fraction” on the y-axis shows two increasing lines. There is an increasing gray line labeled “R” with a similarly increasing red line labeled “NR”, but shifted to the right. This indicates that for a given patient fraction among responders (y-axis), the GIMATS score is higher (right-shifted) for non-responders. This graph points to two outcomes, “predict response to anti-TNF therapy” and identify “potential novel combination targets”.$

IBD | Powered by Mass Cytometry

Single-cell analysis of Crohn’s disease lesions identifies a pathogenic cellular module associated with resistance to anti-TNF therapy

Martin et al. Cell (2019)

Martin et al. use single cell sequencing validated by mass cytometry to identify a 4-subset signature for predictive scoring of response to anti-TNF treatment among patients with ileal Crohn’s disease.

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An infographic. Top section is labeled “observed pattern in tumor” and has two graphs showing number of cells on y-axis and melanoma progression on x-axis. On the left, a horizontal line shows that cDC1 numbers remain constant as melanoma progresses, on the right a downward slope shows that cDC2 numbers decrease. The bottom section is labeled therapeutic approach and shows a DC activation boost using FLT3L + anti-CD40 + Poly I:C results in increased cDC1, cDC2, CD4 T, and CD8 T cells in tumor while tumor-draining lymph node only shows increase in cDC2 and CD8. Arrows between the tumor and draining lymph node show migration of cDC2 from the tumor to the lymph node and recruitment of CD8 T cells from the lymph node to the tumor, indicating the two are interconnected.

Cancer | Powered by Mass Cytometry

Skin dendritic cells in melanoma are key for successful checkpoint blockade therapy

Prokopi et al. JITC (2021)

Prokopi et al. use mass cytometry to track dendritic cell and T cell subsets in both the tumor and tumor-draining lymph nodes and identify distinct patterns of migration and recruitment throughout melanoma progression and treatment.

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Flow chart infographic. At the top are icons representing an NSCLC pre-treatment blood sample in one box. A second box has icons for three immune cell types found in blood: monocytes, NK cells, and T cells. A third box has a 0-3 score running down the left side with scores 0 and 1 corresponding to disease progression and scores 2 and 3 corresponding with better survival in response to anti-PD-1. At the bottom icons representing tubes of blood on the left for scoring are compared “versus” a lung icon for PD-L1 biopsy immuno-stain on the right. Below, a grey to red gradient from left to right is labeled non-invasive on the left and invasive on the right.

Cancer | Powered by Mass Cytometry

Mass cytometry reveals classical monocytes, NK cells, and ICOS+ CD4+ T cells associated with pembrolizumab efficacy in patients with lung cancer

Rochigneux et al. Clin Cancer Res (2022).

Rochigneux et al. identify new blood-based candidate immune cell biomarkers associated with successful clinical outcomes to anti-PD-1 therapy for a subset of NSCLC patients in the KEYNOTE-001 trial.

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Infographic with three rows, each showing CAR T cells and CAR Tregs balanced on a scale. In the top row, CAR T cells outweigh CAR Tregs. Next to them we see icons representing response and neurotoxicity in bright red indicating the clinical outcome. The second row shows CAR Treg outweighing CAR T cells however in this case both the response and neurotoxicity icons are grayed out indicating neither clinical outcome. The bottom row shows CAR T cells weighing just slightly more than CAR Tregs, with only the response icon in red and the neurotoxicity icon grayed out, indicating a balance leaning towards response but without neurotoxicity.

Cancer | Powered by Mass Cytometry

Post-infusion CAR Treg cells identify patients resistant to CD19-CAR therapy

Good et al. Nat Med (2022)

Using mass cytometry, Good et al. profiled post-infusion CAR T cells in patients with large B cell lymphoma and identified a novel population of CAR Tregs associated with disease progression and decreased neurotoxicity.

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Infographic. At the top center is a torso highlighting a breast tumor. An arrow pointing left towards anti-PD-1 says ER. An arrow pointing right towards the words single cell profiling says ER- tumor. In the center are three concentric circles, with labels at three points, and a series of triangles representing tumor profiles showing degree of each feature present for that tumor. The three features are phenotypic abnormality, individuality, and richness. A label indicates that phenotypic abnormality and individuality are associated with poor prognosis. At the bottom is a legend and we see tumors 2 and 3, both with high richness above, are circled and annotated with the text "may respond to anti-PD-1."

Breast | Powered by Mass Cytometry

A single-cell atlas of the tumor and immune ecosystem of human breast cancer

Wagner et al. Cell (2019).

Using mass cytometry, Wagner et al. profile 73 proteins across nearly 200 samples of breast tumor and adjacent non-tumor tissue to identify novel features of tumor composition linked to poor prognosis.

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Flow-style infographic. Starting at the top left we see two human icons, differently colored, then arrows showing both become single cell data, then arrows showing that data is combined into a UMAP plot to generate a hematopoietic classifier. The next stages in the middle row show single-cell classification, dremi scores, and score ranking. This leads to the bottom row which has a large box labeled ML framework, within that is a smaller box with Training, Validation, and Testing, and a circular arrow indicating this is done repeatedly within the ML framework. Finally, this leads to the last sequence which are best scores per cell type, validation, cohort, and predictive survival biomarker with an illustration of a kaplan-meier curve.

Cancer | Powered by Mass Cytometry

Identifying predictors of survival in patients with leukemia using single-cell mass cytometry and machine learning

Kleftogiannis et al. bioRxiv (2022)

Kleftogiannis et al. developed a machine learning framework using mass cytometry data from 45 leukemia patients and identified predictive biomarkers of survival

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A 2D illustration of a grey venn diagram on a white background. One circle is labeled "Thiopurine" and the other is labeled "Anti-TNF". We see red cells, representing B cell subsets, in each with arrows up or down. The Thiopurine side shows Plasma, Memory, and Naive B cells go down. The Anti-TNF side shows all three go up. In the middle, we see that only Naive B go down while the Plasma and Memory effects were canceled out by combination treatment.

IBD | Powered by Mass Cytometry

Deep analysis of the peripheral immune system in IBD reveals new insight in disease subtyping and response to monotherapy or combination therapy

Kosoy et al. CMGH (2021)

Kosoy et al. profiled immune composition of nearly 1200 patients with IBD and over 300 controls using mass cytometry and identified key immune subsets target by combination therapy.

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Infographic with four quadrants and red cells in each quadrant. In the top left quadrant are cells labeled as CD44low Tregs, in the top right quadrant are cells labeled CD44high Tregs. Both quadrants have three cells in them. In between the top and bottom quadrants are the words "injury" and "7 days". In the bottom left quadrant we see only 4 CD44low Tregs, while in the bottom right quadrant we see 7 CD44high Treg cells and they are a darker shade of red indicating activation and proliferation following injury.

Inflammation | Powered by Mass Cytometry

Distinct injury responsive regulatory T cells identified by multi-dimensional phenotyping

Guo et al. Front Immunol (2022)

In Guo et al., the team used mass cytometry in combination with RNA and TCR sequencing to validate two unique clusters of regulatory T cells (Treg) that respond differently to injury.

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2D illustrated thumbnail. At the top, a mouse with an intracerebral tumor receives FLT3 immunotherapy, in the lymph node DCs, NKs, Monocytes, and Tregs go up. At the bottom, these cell types are illustrated with the Tregs shown suppressing the others.

Cancer | Powered by Mass Cytometry

Deep immune profiling reveals targetable mechanisms of immune evasion in immune checkpoint inhibitor-refractory glioblastoma

Simonds EF, Lu ED, Badillo O, et al. JITC (2021)

The team Simonds, Lu, & Badillo et al. used mass cytometry determine why glioblastoma, the most common malignant brain tumor, is also the most resistant to immunotherapy.

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