Stephen Martis’s research while affiliated with Memorial Sloan Kettering Cancer Center and other places

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Publications (7)


A population of colonic Treg cells stably expresses IL-10
a, Frequencies of tdTomato⁺ cells among naive (Tn) (CD44loThy1.1–) and helper (TH) cells (CD44hiThy1.1–), resting (rTreg) (Thy1.1⁺CD62L⁺) and activated (aTreg) (Thy1.1⁺CD62L–) Treg cells (all CD4⁺TCRβ⁺) isolated from tissues of 15-week-old Il10FM mice. medLN, cerLN, and mesLN: mediastinal, cervical, and mesenteric lymph nodes, respectively. LILP: large intestine lamina propria; ND, not detected. Each point represents an individual mouse (n = 10) and data are representative of two independent experiments. Unpaired two-sided t-tests with Holm’s correction for multiple comparisons. b–d,Il10FM mice (7–15 weeks old) were treated with tamoxifen and analyzed 3–56 days later. Mice analyzed together were littermates, treated at different times and analyzed on the same day. Schematic of possible cell states in IL-10 fate-mapping experiments (b). Frequencies of tdTomato⁺ cells among YFP⁺ (c) and YFP⁺ among all (d) Treg cells (Thy1.1⁺CD4⁺TCRβ⁺) isolated from indicated tissues of mice at indicated days after tamoxifen treatment. Each point represents an individual mouse, lines indicate mean per tissue and data are pooled from two independent experiments (n = 4 mice per timepoint). In c, significance was determined by one-way ANOVA with Holm’s correction for multiple comparisons. e,f, Analysis of stability of IL-10 expression after perturbation in 7–10-week-old Il10FM mice. For the bleomycin challenge, mice were first treated with bleomycin or vehicle, then 15 days later treated with tamoxifen and analyzed 21 days later (Bleo). For microbiota depletion, mice were treated with tamoxifen, 3 days later given antibiotics or control drinking water and analyzed 18 days later (Abx). For colitis induction, female mice were treated with tamoxifen, then 10 days later given drinking water containing 3% DSS or vehicle for 7 days and analyzed 8 days later (DSS). Experimental schematics (e) and frequencies of tdTomato⁺ cells among YFP⁺ Treg cells isolated from the lungs (Bleo) or LILP (Abx, DSS) of challenged and littermate control mice (f). Each point represents an individual mouse (n = 6 per group for Bleo; 8–9 per group for Abx and DSS) and data are pooled from two independent experiments. ns, P > 0.05; **P < 0.01, ****P < 0.0001.
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Distinct transcriptional features of Il10neg and Il10stable cells
RNA was isolated from Il10neg, Il10recent and Il10stable Treg cells, as defined in Fig. 1b, sorted from the colonic lamina propria of 10-week-old Il10FM mice treated 21 days earlier with tamoxifen and sequenced. a,b, Coloring depicts Z-score normalized log2-transformed gene FPKM counts for individual Il10neg and Il10stable samples. Genes shown are all significantly differentially expressed between Il10neg and Il10stable (log2FC > 1 and adjusted P < 0.05), annotated as encoding cell surface or secreted proteins and manually categorized (a) or select differentially expressed (adjusted P < 0.05) TFs (b). c, K-means clustering was performed on Z-score-normalized log2-transformed gene FPKM counts for genes significantly differentially expressed in any pairwise comparison (P < 0.05). Genes of interest differentially expressed between Il10stable versus Il10neg (black) or Il10stable versus Il10neg and Il10recent (red) within clusters I, II, IV and V are indicated. See Methods for details. Negative binomial fitting with two-sided Wald’s significance test and the Benjamini–Hochberg correction for multiple comparisons. Significance testing and correction were performed on all genes.
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RNA-seq analysis of colonic Treg cells indicating terminal differentiation of Il10stable cells
tdTomato⁺ and tdTomato– Treg cells (Thy1.1⁺CD4⁺TCRβ⁺) were separately sorted from the colon (LILP) and mesLN of Il10FM mice and processed for scRNA-seq. See Methods for details. Bulk RNA-seq data is from the analysis presented in Fig. 2. a,b, Two-dimensional force-directed graph layouts of tdTomato⁺ and tdTomato– (a) Treg cells from the LILP and lymph node or colored according to cluster (b). c, Integration of bulk and single-cell RNA-sequencing. Mean log2-transformed FPKM counts were computed for each k-means gene cluster for each bulk RNA-seq sample, and mean expression was then Z-score-normalized across samples per cluster. The scRNA-seq cells were scored for expression of bulk RNA-seq gene clusters, and the 11 nearest-neighbor cell clusters were then separated as originating from the tdTomato⁺ or tdTomato– sample and manually organized. Per-subset expression score was Z-normalized across cell clusters per gene cluster. For clarity, scRNA-seq populations with very few cells are not depicted. Colored boxes indicate bulk RNA-seq samples sorted as Il10neg, Il10recent or Il10stable Treg cell populations, and scRNA-seq populations by cluster and cell sample origin (tdTomato⁺ or tdTomato–). See Methods for details. d,e, Plots depicting two-dimensional force-directed graph layout for all tdTomato⁺ and tdTomato– LILP and lymph node scRNA-seq cells. Coloring indicates manually determined similarity of gene expression to the bulk-sorted l10neg (blue), Il10recent (orange) and Il10stable (yellow) Treg cell populations or whether cells derive from the lymph node (light grays) or belong to a colonic ‘myeloid-like’ T cell population (dark gray) (d). Shading (color bar) indicates entropy, as determined by the Palantir algorithm (e). See Methods for details.
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Characterization of IL-10⁺ and IL-10⁻ Treg cells by scRNA-seq
a, tdTomato⁺ and tdTomato– Treg cells (Thy1.1⁺CD4⁺TCRβ⁺) were separately sorted from the colon (LILP) and mesLN of Il10FM mice and scRNA-seq was performed; two-dimensional force-directed graph layout of all tdTomato⁺ and tdTomato– LILP and lymph node scRNA-seq cells. Shading indicates gene expression score for genes associated with the S phase of the cell cycle. b, Bulk RNA-seq data from the analysis presented in Fig. 2. Heatmap showing log2-transformed, row Z-score-normalized FPKM counts for genes associated with the S phase of the cell cycle among bulk RNA-seq samples. c, tdTomato⁺ and tdTomato– Treg cells (Thy1.1⁺CD4⁺TCRβ⁺) were separately sorted from the colon lamina propria (LILP), mesLN and lung of Il10FM mice and scRNA-seq was performed. Data are from scRNA-seq analysis presented in Fig. 3, with the addition of cells from the lung. two-dimensional force-directed graph layout of all tdTomato⁺ and tdTomato– scRNA-seq cells. In the left panel, coloring indicates the assigned nearest-neighbor cluster for each cell when clustering LILP and lymph node cells, as in Fig. 3a; right panel coloring indicates cells sorted as tdTomato⁺ or tdTomato– from the lung.
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Characterization of colonic Treg cell subsets by integrated RNA-seq and ATAC–seq analysis
Il10neg, Il10recent and Il10stable Treg cells, as defined in Fig. 1b, were sorted from the LILP of 10-week-old Il10FM mice treated 21 days earlier with tamoxifen and subjected to ATAC–seq analysis. Bulk RNA-seq data is from the analysis presented in Fig. 2. a, Plot showing mean log2FC peak accessibility for Il10stable versus Il10neg (x axis) and Il10stable versus Il10recent (y axis) samples. Coloring indicates peaks significantly differentially accessible (adjusted P < 0.05) in Il10stable versus Il10neg (red outline), Il10stable versus Il10recent (black fill), both (black fill: red outline) or neither (gray) comparisons. Negative binomial fitting with two-sided Wald’s significance test and the Benjamini–Hochberg correction for multiple comparisons. b, See Methods for model generation and coefficient determination. Plots showing per-motif coefficients for the svn (y axis) and svr (x axis) models. Coloring indicates motifs with significant (P < 0.001) coefficients: in neither (gray), svn (black fill), svr (red outline) or both (black fill, red outline) models. See Methods for modeling and significance testing details. c. Mean log2-transformed FPKM RNA or ATAC tag counts were computed for each k-means gene cluster for each bulk RNA-seq or ATAC–seq sample. Mean expression or accessibility was then Z-score normalized across samples per cluster. Shading indicates Z-score-normalized expression or accessibility count means. Pearson’s correlation coefficients were calculated for expression versus accessibility FC for the genes and associated peaks in each cluster. Shading indicates correlation coefficient. Clusters with the highest correlation for comparison (green, bI–svr; orange, bIV–svn) are boxed. d,e, See Methods for details. x axes, difference between original and motif withheld correlation coefficients (Δcor); y axes, log2FC peak accessibility for cluster bIV (d) and cluster bI (e) associated peaks containing each motif. Dashed line indicates log2FC peak accessibility for all cluster bIV (d) and cluster bI (e) associated peaks. Shading indicates coefficients for the svn (d) and svr (e) models. Highlighted quadrants with motifs positively contributing to the model’s predictiveness and associated with above-average increased (orange, d) or decreased (green, e) accessibility.
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Terminal differentiation and persistence of effector regulatory T cells essential for preventing intestinal inflammation
  • Article
  • Full-text available

February 2025

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13 Reads

Nature Immunology

Stanislav Dikiy

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Aazam P. Ghelani

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Andrew G. Levine

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[...]

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Alexander Y. Rudensky

Regulatory T (Treg) cells are a specialized CD4⁺ T cell lineage with essential anti-inflammatory functions. Analysis of Treg cell adaptations to non-lymphoid tissues that enable their specialized immunosuppressive and tissue-supportive functions raises questions about the underlying mechanisms of these adaptations and whether they represent stable differentiation or reversible activation states. Here, we characterize distinct colonic effector Treg cell transcriptional programs. Attenuated T cell receptor (TCR) signaling and acquisition of substantial TCR-independent functionality seems to facilitate the terminal differentiation of a population of colonic effector Treg cells that are distinguished by stable expression of the immunomodulatory cytokine IL-10. Functional studies show that this subset of effector Treg cells, but not their expression of IL-10, is indispensable for colonic health. These findings identify core features of the terminal differentiation of effector Treg cells in non-lymphoid tissues and their function.

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IL33 expression correlates with TLS transcriptional signatures in human tumours
a,b, Unbiased correlation of bulk tumour mRNA gene expression to TLS transcriptional signatures (TLS signatures 1 (ref. ⁴), 2 (ref. ⁷) and 3 (ref. ⁸)) in human PDAC (a, top). Correlation of bulk tumour IL33 mRNA expression to TLS transcriptional signatures and LTB in human PDAC (a, bottom) and human tumours (TLS signature 1; b). c,d, Immunohistochemistry (c, left) and immunofluorescence (c, right), and the immunofluorescence-quantified frequency of bulk IL-33⁺ cells (c) and IL-33⁺CD45⁺ cells (d), and association with survival (c and d) in human PDAC. The dotted red line in c shows the putative TLS. Adj. panc., adjacent pancreatic tissue; HR, hazard ratio. n represents the number of tumours (a–c) or patients (survival; c and d). High and low (in the survival plots in c and d) represent ≥ (high) or < (low) the median frequency of the cohort. Horizontal bars in dot plots indicate the median (c, d). P values were calculated using two-sided Pearson correlation (a and b), two-tailed Mann–Whitney U-test (c (left) and d (left and middle)) and two-sided log-rank test (c (right) and d (right)). For c, scale bars, 20 μm (left) and 50 μm (right).
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The alarmin domain of IL-33 induces TLSs in PDAC and chemical colitis
a, Haematoxylin and eosin staining (H&E) (top) and quantification of intratumoural TLSs (bottom left) and intracolonic lymphoid aggregates (LA; bottom right) in anti-LTβR-treated PDAC (left) and DSS-colitis (right) WT and Il33−/− mice. b, H&E (top) and immunofluorescence (IF; middle) staining, and the TLS density (H&E quantified; bottom) in vehicle (veh.) and rIL-33-treated PDAC mice. PDAC 1–6, orthotopic PDAC mice established with six PDAC cell lines. c,d, Intratumoural TLS maturation states (immunofluorescence quantified) (c), and the B cell frequency and clonality and somatic hypermutation (d) in vehicle-treated and rIL-33-treated PDAC mice. e, H&E and colonic LA quantification in vehicle-treated and rIL-33-treated DSS-colitis mice. Data were collected 2 weeks (a, left), 3 weeks (b, PDAC 3 and 4; c and d) and 3–5 weeks (b, PDAC 1, 2, 5 and 6) after tumour implantation, and 2 weeks after DSS initiation (a (right) and e), pooled from ≥2 independent experiments with n ≥ 2 mice per group with consistent results. n represents the number of individual tumours or organs from individual mice analysed separately, or B cell clones (d, right). The dotted lines in a, b and e show putative TLS/LA; the horizontal bars in a–e show the median. P values were calculated using two-way analysis of variance (ANOVA) with Tukey’s multiple-comparison test (a) and two-tailed Mann–Whitney U-test (b–e). Scale bars, 20 μm (a, b (top) and e) and 10 μm (b (bottom)).
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IL-33 activates lymphoneogenic ILC2s
a, Single-cell RNA-sequencing (scRNA-seq) analysis of purified tumour and DLN ILC2s from rIL-33-treated PDAC mice. The uniform manifold approximation and projection (UMAP) representations show cells by unsupervised clusters (top left), organs (bottom left) and select genes (right). The heat map (middle) shows the top 25 genes by cluster. The heat map scale bar represents the z score. The UMAP scale bar represents gene expression. b, Gating (left) and LT expression by flow cytometry (right) and confocal imaging (bottom) in intratumoural KLRG1⁺ ILC2s from rIL-33-treated WT and Ltb−/− PDAC mice. FMO, fluorescence minus one; MFI, mean fluorescence intensity. c,d, Gating, and the frequency and number of intratumoural (c) and gut (d) ILC2s in vehicle-treated and rIL-33-treated PDAC (c) and DSS-colitis (d) mice. e, Gating (left), ILC2 frequency (middle) and intratumoural TLS density (right) in rIL-33 and DT-treated Nmur1icre-eGFPRosa26LSL-DTR and littermate control Rosa26LSL-DTR PDAC mice. f, Intratumoural TLS density (left), gating, frequency and the number of ILC2s, and the number of DLN immune cells (right) in rIL-33-treated Nmur1icre-eGFPLtbfl/fl and littermate control PDAC mice. g, Tumour KLRG1⁺ ILC2s were sort-purified from rIL-33-treated Il7rcre/+Ltbfl/fl or Il7rcre/+Ltb+/+ littermate PDAC mice and transferred to Il7rcre/+Rorafl/fl PDAC mouse recipients. The TLS density, KLRG1⁺ ILC2 frequency and number in recipient PDACs. Data were collected at 10 days (a), 2 weeks (b and d–f) and 5 weeks (c) after tumour implantation, and 2 weeks (g) after transfer, pooled from ≥3 independent experiments with n ≥ 3 mice per group with consistent results. n represents the number of individual tumours or organs from individual mice analysed separately. The horizontal bars show the median. In f, littermate controls include Ltbfl/+ and Nmur1icre-eGFPLtbfl/+ combined. P values were calculated using two-tailed Mann–Whitney U-test (b, e (right), f and g) and two-way ANOVA with Holm’s test (c, d and e (left)). Scale bars, 1 μm (b (left)), 5 μm (b (all others)), 20 μm (e and f (left)) and 100 μm (f (right)).
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Lymphoneogenic ILC2s migrate to tumours through a microbiota- and PDAC-modulated gut–blood circuit
a, Parabiotic mice with PDACs in recipient pancreata. Donors were treated with vehicle or rIL-33. Quantification (top) and gating (bottom) of donor-derived ILC2s in recipient blood and PDAC. b, KikGR PDAC mice were treated daily with rIL-33 for 6 days. On day 6, the gut or peritoneum was photoconverted. Experimental schematic (left) and density of gut-derived (RFP⁺) ILC2s in pancreatic PDACs (right). c, Gut Il33 mRNA was analysed by digital droplet PCR in sham-treated and PDAC mice. d, Gut Il33 mRNA (left), blood KLRG1⁺ ILC2s (middle) and the intratumoural TLS density (right) in rIL-33-treated PDAC mice treated with or without antibiotics (Abx). e, The intratumoural KLRG1⁺ ILC2 frequency, number and TLS density in skin (s.c.) PDACs in vehicle-treated and rIL-33-treated mice with s.c. alone (single PDAC) or s.c. and pancreatic PDACs (dual PDAC). f, Parabiotic mice with s.c. PDACs in recipients with or without pancreatic PDACs in donors. Donors were treated with vehicle or rIL-33. The donor-derived ILC2 frequency in recipient s.c. PDACs (bottom) is shown. g, Parabiotic mice with s.c. PDACs in recipients and pancreatic PDACs in donors. Donors were treated with rIL-33; donor and recipients were treated with antibiotics. The donor-derived ILC2 number and frequency in recipient s.c. PDACs and gut (bottom) are shown. Data were collected at 2 weeks (a, e (left), f and g), as shown in the experimental schema (b), 4 weeks (c and d) and 5 weeks (e (right)) after tumour implantation, pooled from ≥2 independent experiments with n ≥ 3 mice per group with consistent results. n values represent the number of individual tumours or organs from individual mice analysed separately. The horizontal bars show the median. P values were calculated using two-tailed Mann–Whitney U-test (a, c, d, e (right) and g), one-way ANOVA with Kruskal–Wallis multiple-comparison test (b), two-way ANOVA with Kruskal–Wallis test (d, middle) and two-way ANOVA with Sidak’s multiple-comparison test (e (left) and f). Scale bar, 20 μm (e).
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An engineered human IL-33 therapeutic stimulates TLSs and anti-tumour activity in PDAC
a, ST2 activation in HEK blue human IL-33 reporter cells with H-rIL-33, H-e-rIL-33 and H-e-rIL-33–Fc. EC50, half-maximal effective concentration. b–d, The intratumoural KLRG1⁺ ILC2 frequency (b), TLS density (c) and tumour volume (d) in PDAC mice treated with vehicle, mouse rIL-33, H-rIL-33 or escalating H-e-rIL-33–Fc. Curves in a were fit by nonlinear regression. Data were collected at 3–4 weeks (b and c) after tumour implantation, pooled from ≥2 independent experiments with n ≥ 3 mice per group with consistent results. n values represent the number of individual tumours from individual mice. The horizontal bars show the median. P values were calculated using extra-sum-of-squares F test (a) and two-way ANOVA with Tukey’s multiple-comparison correction test (b–d).
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IL-33-activated ILC2s induce tertiary lymphoid structures in pancreatic cancer

January 2025

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67 Reads

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1 Citation

Nature

Tertiary lymphoid structures (TLSs) are de novo ectopic lymphoid aggregates that regulate immunity in chronically inflamed tissues, including tumours. Although TLSs form due to inflammation-triggered activation of the lymphotoxin (LT)–LTβ receptor (LTβR) pathway¹, the inflammatory signals and cells that induce TLSs remain incompletely identified. Here we show that interleukin-33 (IL-33), the alarmin released by inflamed tissues², induces TLSs. In mice, Il33 deficiency severely attenuates inflammation- and LTβR-activation-induced TLSs in models of colitis and pancreatic ductal adenocarcinoma (PDAC). In PDAC, the alarmin domain of IL-33 activates group 2 innate lymphoid cells (ILC2s) expressing LT that engage putative LTβR⁺ myeloid organizer cells to initiate tertiary lymphoneogenesis. Notably, lymphoneogenic ILC2s migrate to PDACs from the gut, can be mobilized to PDACs in different tissues and are modulated by gut microbiota. Furthermore, we detect putative lymphoneogenic ILC2s and IL-33-expressing cells within TLSs in human PDAC that correlate with improved prognosis. To harness this lymphoneogenic pathway for immunotherapy, we engineer a recombinant human IL-33 protein that expands intratumoural lymphoneogenic ILC2s and TLSs and demonstrates enhanced anti-tumour activity in PDAC mice. In summary, we identify the molecules and cells of a druggable pathway that induces inflammation-triggered TLSs. More broadly, we reveal a lymphoneogenic function for alarmins and ILC2s.


Abstract 4093: Modeling tumor immunoediting under immune selective pressure to inform neoantigen landscape dynamics for effective cancer vaccines

March 2024

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7 Reads

Cancer Research

Tumor specific neoantigens, encoded by somatic mutations, are recognized by T cells, inducing anti-tumor immune responses. This renders neoantigens viable targets for personalized cancer vaccines. However, the identification of immunogenic neoantigens remains suboptimal. Understanding how immune selective pressure-mediated tumor evolution and neoantigen editing contribute to emergence of immunogenic neoantigens may improve prediction accuracy. In this study, we aimed to model tumor evolution and heterogeneity under immune pressure using three preclinical models of lung cancer with common driver mutations, Kras G12D/+ and p53 -/-, namely KPA, KPC, and HKP1, subcutaneously implanted in immunocompetent C57BL/6 (B6) and immune-deficient Rag1-/- mice. Our data suggest that despite shared driver mutations identified by whole exome and RNA-seq, tumor growth in vivo varied between tumor models. KPA tumors were immunogenic, as they were controlled in B6 mice but rapidly progressed in Rag1-/- mice, suggesting immunoediting of KPA-specific neoantigens. In contrast, HKP1 was non-immunogenic, with tumors progressing regardless of immune competency. KPC showed moderate tumor control in B6 mice, which was lost in Rag1-/- mice. We then estimated the cancer cell fraction on every mutation based on phylogenetic reconstruction. Immunogenic KPA tumors had significantly lower mutation burden in B6 than in Rag1-/- mice and the fraction of cell line mutations edited in vivo was significantly higher, suggesting active immunoediting in the former. Non-immunogenic HKP1 tumors did not show significant differences in mutation burden nor fraction of mutations edited between B6 and Rag1-/- mice. The immunogenicity was reflected in the immune infiltrate levels within tumors, with KPA being highly infiltrated by activated CD4 and CD8 T cells compared to KPC. HKP1 tumors showed increased infiltration of suppressive regulatory T cells. T cell receptor (TCR) sequencing on lung tumors showed that clonal expansion is strongly associated with immunogenicity. KPA tumors showed significantly higher TCR clustering with less diversity compared to KPC and more than HKP1. Following a simple approach to study the immunogenicity of shared neoantigens to elicit cross-protective anti-tumor responses, mice were immunized with whole irradiated (IR) tumor cell lines and implanted with live tumor cells (matched and unmatched). While immunization with IR-KPA protected against corresponding tumor implant and moderately against KPC, it did not protect against HKP1, indicating lack of immunogenicity of mutations shared between KPA and HKP1. Further studies testing the anti-tumor responses and characteristics of neoantigens shared between these three related tumor cell lines will inform the conditions under which tumors may escape or regress and help improve neoantigen identifying algorithms. Citation Format: Mariam Mathew George, Jayon Lihm, Hyejin Choi, Yuval Elhanati, Stephen Martis, Marta Luksza, Benjamin Greenbaum, Jedd D. Wolchok, Taha Merghoub. Modeling tumor immunoediting under immune selective pressure to inform neoantigen landscape dynamics for effective cancer vaccines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4093.


Abstract 4440: Topographical investigation of metabolites in excised squares (TIMES2): Comprehensive cross-sectional metabolite quantification of pancreatic cancer in vivo

March 2024

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8 Reads

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1 Citation

Cancer Research

Purpose: Pancreatic cancer is a lethal malignancy characterized by complex intratumoral metabolic reprogramming and intercellular nutrient sharing between cells in the tumor microenvironment (TME) that promote pancreatic cancer progression. However, this crosstalk, as well as regional variation in perfusion and oxygenation, can lead to metabolic heterogeneity that has not been appreciated by metabolomics of whole tumors. Here we quantify amino acids and tricarboxylic acid cycle (TCA) intermediates using a novel methodology that allows us to portray global tumor metabolite heterogeneity in a tumor. Methods: Human PaTu-8902 or murine HY19636 (from female KPC mice LSL-KrasG12D; p53 L/+, Ptf1a-Cre+) pancreatic cancer cell lines were orthotopically injected into pancreata of NCr nude mice (n=3) or C57BL/6 mice (n=2). Mice were euthanized after 3-5 weeks and tumors were harvested. Tumor slices were further sectioned into 1mm x 1mm x 1mm cubes using a custom-made multisectional slicing device and each cube location was recorded. Each cube was extracted using methanol, water, and chloroform with labelled amino acid standards, derivitized, and resolved using gas chromatography-mass spectrometry (DB-35MS column with Agilent 7890B gas chromatograph coupled to a single quadrupole 5977B mass spectrometer). 22 metabolites (15 amino acids, 5 TCA intermediates, lactate, and pyruvate) were identified by unique fragments and retention time compared to known standards. Peaks were picked using OpenChrom and analyzed using MATLAB. Data was analyzed using Graphpad Prism. Principal Component Analysis (PCA) was visualized using Python on a Jupyter notebook. Results: Both orthotopic human and murine pancreatic tumors demonstrated striking levels of intratumoral metabolite heterogeneity. Glycine, glutamine, and proline were the amino acids with the highest coefficient of variance, while leucine, isoleucine, and serine had the lowest coefficient of variance. α-ketoglutarate and succinate were the TCA intermediates with highest coefficient of variance. Lactate had the lowest coefficient of variance among all examined metabolites. Spatial mapping of each metabolite demonstrated distinct regions with varying abundance levels of metabolites. PCA demonstrated 75% of variance was carried by PC1 and 10% carried by PC2. Conclusions: This study reveals insights into the degree of intratumoral heterogeneity present in pancreatic tumors that illustrate the difficulty of in vivo metabolomics analysis and suggest that high-resolution (single cell) metabolomics techniques will be critical to study metabolism in the complex TME. Citation Format: Peter Yu, Robert Banh, Albert Sohn, Stephen Martis, Douglas Biancur, Keisuke Yamamoto, Elaine Lin, Alec Kimmelman. Topographical investigation of metabolites in excised squares (TIMES2): Comprehensive cross-sectional metabolite quantification of pancreatic cancer in vivo [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4440.


Abstract C054: Topographical investigation of metabolites in excised squares (TIMES2): Mapping in vivo of metabolic heterogeneity in pancreatic tumors

January 2024

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10 Reads

Cancer Research

Background: Pancreatic cancer is a lethal malignancy characterized by complex intratumoral metabolic reprogramming and intercellular nutrient sharing between cells in the tumor microenvironment (TME) that promote pancreatic cancer progression. However, this crosstalk, as well as regional variation in perfusion and oxygenation, can lead to metabolic heterogeneity that has not been appreciated by metabolomics of whole tumors. Here we quantify amino acids and tricarboxylic acid cycle (TCA) intermediates using a novel methodology that allows us to portray global tumor metabolite heterogeneity in a tumor. Methods: Human PaTu-8902 or murine HY19636 (from female KPC mice p48-Cre+, KRASLSL-G12D/+, Trp53lox/+) pancreatic cancer cell lines were orthotopically injected into pancreata of NCr nude mice (n=3) or C57BL/6 mice (n=2). Mice were euthanized after 3-5 weeks and tumors were harvested. Tumor slices were further sectioned into 1mm x 1mm x 1mm cubes using a custom-made multisectional slicing device and each cube location was recorded. Each cube was extracted using methanol, water, and chloroform with labelled amino acid standards, derivitized, and resolved using gas chromatography-mass spectrometry (DB-35MS column with Agilent 7890B gas chromatograph coupled to a single quadrupole 5977B mass spectrometer). 22 metabolites (15 amino acids, 5 TCA intermediates, lactate, and pyruvate) were identified by unique fragments and retention time compared to known standards. Peaks were picked using OpenChrom and analyzed using MATLAB. Data was analyzed using Graphpad Prism. Principal Component Analysis (PCA) was visualized using Python on a Jupyter notebook. Results: Both orthotopic human and murine pancreatic tumors demonstrated striking levels of intratumoral metabolite heterogeneity. Glycine, glutamine, and proline were the amino acids with the highest coefficient of variance, while leucine, isoleucine, and serine had the lowest coefficient of variance. α-ketoglutarate and succinate were the TCA intermediates with highest coefficient of variance. Lactate had the lowest coefficient of variance among all examined metabolites. Spatial mapping of each metabolite demonstrated distinct regions with varying abundance levels of metabolites. PCA demonstrated 75% of variance was carried by PC1 and 10% carried by PC2. Conclusions: This study reveals insights into the degree of intratumoral heterogeneity present in pancreatic tumors that illustrate the difficulty of in vivo metabolomics analysis and suggest that high-resolution (single cell) metabolomics techniques will be critical to study metabolism in the complex TME. Citation Format: Peter Yu, Robert Banh, Stephen Martis, Douglas Biancur, Albert Sohn, Elaine Lin, Keisuke Yamamoto, Benjamin Greenbaum, Alec Kimmelman. Topographical investigation of metabolites in excised squares (TIMES2): Mapping in vivo of metabolic heterogeneity in pancreatic tumors [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr C054.


968 PD-1 hi Foxp3 - CD4 + tumor-infiltrating T-cell lineage commitment impacts the immunotherapy outcome

Background In contrast to PD-1⁺CD8⁺ T cells, PD-1⁺CD4⁺ T cells and their impact in tumor progression and immunotherapy response remain relatively unexplored. We previously reported that PD-1hiFoxp3⁻CD4⁺ T cells (4PD1hi) from melanoma-bearing mice and patients with melanoma or non-small cell lung cancer (NSCLC) suppress T-cell function and correlate with unfavorable outcomes upon immune checkpoint blockade (ICB) therapy.¹ CD4⁺PD-1⁺ T cells were also found to correlate with poor prognosis in other NSCLC patient cohorts.2–4 We showed that 4PD1hiup-regulate T-follicular-helper-cell(Tfh)-related genes.¹ 4PD1hi cells suppressing immunotherapy responses were recently described in mouse sarcoma models; however, in this setting, 4PD1hi did not over-express Tfh genes.⁵ Here, we sought to deconvolve the lineage commitment of 4PD1hi tumor-infiltrating lymphocytes (TILs) in relationship with their immune function and impact on ICB outcome. Methods Single-cell RNA-sequencing (scRNAseq) was performed in 4PD1hi, PD-1⁻Foxp3⁻CD4⁺ (4Dneg), and Foxp3⁺CD4⁺ T cells (Tregs) FACS-sorted from ICB-treated B16F10-melanoma bearing Foxp3-GFP mice. scRNAseq datasets of TILs from ICB-treated cancer patients6–9 were used to extract 4PD1hi and analyze their profiles. Tfh-deficient SAP knock-out (KO) and CD4KO:CXCR5KO mixed bone marrow (BM) transplanted RAGKO and control mice were implanted with B16F10, treated with ICB, and 4PD1hi, 4Dneg, and Tregs were quantified by flow cytometry and FACS-sorted for functional analyses. Results Using prior-knowledge-based signatures and mutual-information-based cell-type classification,¹⁰ we found that spleen-derived 4PD1hi cells from tumor-bearing mice polarize toward Tfh, Tregs toward a canonical Treg phenotype, and 4Dneg toward Th1. Conversely, tumor-derived 4PD1hi were not significantly skewed toward these phenotypes but gained in Th1 polarization after an effective anti-CTLA-4 treatment. In human primary melanoma,⁷ NSCLC,⁶ and squamous/basal cell carcinoma,⁸ 4PD1hi cells over-expressed Tfh-related genes. This was less clear in 4PD1hi from mixed NSCLC samples, encompassing primary tumors and different metastatic sites.⁹ However, 4PD1hi from ICB-non-responder patients in this study⁹ displayed the greatest Tfh-signature scores. Consistently, 4PD1hi TILs from ICB-non-responders in the other NSCLC and melanoma datasets up-regulated Tfh-related genes. To test 4PD1hi TIL Tfh polarization in ICB response, we used SAPKO and CD4KO:CXCR5KO BM chimera mice. Both Tfh-deficient models showed better tumor responses to a suboptimal anti-CTLA-4 treatment¹; however, 4PD1hi TILs did not substantially decrease. In this setting, 4PD1hi TILs lost suppressive function, down-regulated Pdcd1 and Il10, and up-regulated Ifng, suggesting a Th1 phenotypic switch. Conclusions These results indicate that 4PD1hi TILs are heterogeneous and their Tfh/Th1 polarization influences immunotherapy responses possibly in a tumor-tissue dependent way. In melanoma, 4PD1hi TIL Tfh polarization drives immunosuppression and ICB resistance. Acknowledgements This study was supported in part by the Parker Institute for Cancer Immunotherapy. S. M. and A.O. contributed equally to this work. References • Zappasodi R, Budhu S, Hellmann MD, Postow MA, Senbabaoglu Y, Manne S, et al. Non-conventional Inhibitory CD4(+)Foxp3(-)PD-1(hi) T Cells as a Biomarker of Immune Checkpoint Blockade Activity. Cancer Cell. 2018; 33 (6):1017–32 e7. • Zheng H, Liu X, Zhang J, Rice SJ, Wagman M, Kong Y, et al. Expression of PD-1 on CD4 + T cells in peripheral blood associates with poor clinical outcome in non-small cell lung cancer. Oncotarget. 2016; 7 (35). • Arrieta O, Montes-Servín E, Hernandez-Martinez J-M, Cardona AF, Casas-Ruiz E, Crispín JC, et al. Expression of PD-1/PD-L1 and PD-L2 in peripheral T-cells from non-small cell lung cancer patients. Oncotarget. 2017; 8 (60). • Duchemann B, Naigeon M, Auclin E, Ferrara R, Cassard L, Jouniaux JM, et al. CD8(+)PD-1(+) to CD4(+)PD-1(+) ratio (PERLS) is associated with prognosis of patients with advanced NSCLC treated with PD-(L)1 blockers. J Immunother Cancer. 2022; 10 (2). • Hussein S, Kelly M, Yuang S, Samuel A, Ton S, Yik Andy Y, et al. 578 CD8-targeted IL-2 drives potent anti-tumor efficacy and promotes action of tumor specific vaccines. Journal for ImmunoTherapy of Cancer. 2021; 9 (Suppl 2):A607. • Caushi JX, Zhang J, Ji Z, Vaghasia A, Zhang B, Hsiue EH, et al. Transcriptional programs of neoantigen-specific TIL in anti-PD-1-treated lung cancers. Nature. 2021; 596 (7870):126–32. • Schad SE, Chow A, Mangarin L, Pan H, Zhang J, Ceglia N, et al. Tumor-induced double positive T cells display distinct lineage commitment mechanisms and functions. J Exp Med. 2022; 219 (6). • Yost KE, Satpathy AT, Wells DK, Qi Y, Wang C, Kageyama R, et al. Clonal replacement of tumor-specific T cells following PD-1 blockade. Nat Med. 2019; 25 (8):1251–9. • Liu B, Hu X, Feng K, Gao R, Xue Z, Zhang S, et al. Temporal single-cell tracing reveals clonal revival and expansion of precursor exhausted T cells during anti-PD-1 therapy in lung cancer. Nature Cancer. 2022; 3 (1):108–21. • Ceglia N, Sethna Z, Freeman SS, Uhlitz F, Bojilova V, Rusk N, et al. GeneVector: Identification of transcriptional programs using dense vector representations defined by mutual information. bioRxiv. 2023:2022.04.22.487554.


Abstract 860: The dynamics of LINE-1 retrotransposition in cellular populations

April 2023

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6 Reads

Cancer Research

Retrotransposons are genetic elements that have the ability to copy and paste themselves in the genome via the proteins they encode. Since the rapid growth of a retrotransposon can be highly deleterious at the organismal level, many of these genetic parasites are suppressed or have been inactivated over evolutionary time. However, the human genome still hosts the active retrotransposon LINE-1, whose aberrant expression is associated with various diseases, especially epithelial cancers. Despite these associations, a quantitative understanding of the dynamics and diversity of LINE-1 insertions in somatic tissue is lacking. In order to address this gap, we present a population dynamic model of LINE-1 retrotransposition in individual cells. Using the model, we show that at demographic equilibrium, the LINE-1 copy number distribution is broader than would be expected from classical population genetic models. We demonstrate how this broadening distorts the frequency spectrum of insertions. We compare the model's predictions to data from whole genome sequencing of individual tumor cells. Citation Format: Stephen Martis, Alexander Solovyov, Jayon Lihm, Hao Li, Benjamin Greenbaum. The dynamics of LINE-1 retrotransposition in cellular populations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 860.

Citations (1)


... Одной из важных неподвижных фаз, селективность которой отличается от селективности стандартных неподвижных фаз, является 35%-фенил-метилполиси-локсан. Эти неподвижные фазы (например, DB-35, DB-35MS, OV-35) широко используются в аналитической практике [12][13][14], в том числе в новейших работах [15][16]. Работ по прогнозированию удерживания для них крайне мало, в частности, есть работы по прогнозированию удерживания замещенных полихлорированных бифенилов [17][18]. ...

Reference:

Эмпирические уравнения для прогнозирования газохроматографических индексов удерживания для неподвижной фазы DB-35MS
Abstract 4440: Topographical investigation of metabolites in excised squares (TIMES2): Comprehensive cross-sectional metabolite quantification of pancreatic cancer in vivo
  • Citing Article
  • March 2024

Cancer Research