Title: High dimensional single cell analysis predicts response to anti-pd-1 immunotherapy. SUPPLEMENTARY INFORMATION - Content list

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1 Title: High dimensional single cell analysis predicts response to anti-pd- immunotherapy Authors: Carsten Krieg*, Malgorzata Nowicka,, Silvia Guglietta, Sabrina Schindler 5, Felix J. Hartmann, Lukas M. Weber,, Reinhard Dummer 5, Mark D. Robinson,, Mitchell P. Levesque # * 5, Burkhard Becher # *. SUPPLEMENTARY INFORMATION - Content list METHODS Patient samples Stimulations, stainings, and mass cytometry acquisition Antibody Conjugation CyTOF Data Analysis Cytokine analysis based on a bimatrix CellCnn analysis Validation by flow cytometry + correlation of PFS with monocyte frequency Patient data and analysis Immunohistology TABLES AND FIGURES Table S Blood samples characteristics biomarker discovery study. Table S Staining panels for Mass Cytometry Data Sets. Table S Blood sample characteristics for the validation study. Figure S Experimental design used for the CyTOF data. Figure S Simultaneous detection of T cell differentiation and activation markers in blood. Figure S Immunohistochemical intensity scores of lyphocytes and monocytes markers in FFPE melanoma tumor samples. Figure S Defining CD8 + T cells subpopulations by using over-clustering. Figure S5 Simultaneous detection of TH and CTL profiles in human blood. Figure S6 Characterization of the circulating myeloid compartment. Figure S7 Comparison of frequencies in cellular sub-populations using the myeloid panel Figure S8 Correlation of IFN-γ-producing T cells with myeloid cell expansion. Figure S9 In depth analysis of the CD+ myeloid compartment. Figure S Identification of a monocyte signature by CellCnn Figure S Back projection of cells identified using CellCnn into tsne. Figure S CXCL expression by RNA-Seq. Figure S Citrus analysis of the T cell panel. Figure S Citrus analysis of the myeloid compartment Figure S5 FACS validation panel Figure S6 Baseline clinical characteristics of all patients. Figure S7 Hazard rates at baseline of all patients REFERENCES Nature Medicine: doi:.8/nm.66

2 Krieg et al. - HighDim analysis predicts response to PD- therapy supplementary data SUPPLEMENTARY METHODS Patient Samples As healthy controls (n=), age- and sex-matched PBMCs were acquired from the Red Cross Blood Bank, Zurich, Switzerland. Two patient cohorts (total n=5) were analyzed in this study (see Supplementary Tables and ). Baseline was defined as a sample that was collected within a maximum of.5 months of therapy initiation, with an average of.7 days before the therapy started and a median of days, meaning that the majority of baseline samples were collected on the day of treatment initiation. All human biological samples were collected after written informed consent of the patients and with approval of the Local Ethics Committee (Kantonale Ethikkommission Zürich, KEK-ZH authorization Nr. -5) in accordance to GCP guidelines and the Declaration of Helsinki. Stimulations, stainings, and mass cytometry acquisition Cryopreserved PBMCs were thawed, incubated for minutes in pre-warmed complete RPMI (RPMI, % FBS, Glutamine, Penicillin and Streptomycin) containing µg/ml DNAse, spun down, washed in crpmi. Cells from each sample were washed, counted, adjusted to xe6 life cells/stain and seeded in 96-well plates. For stimulations cells were seeded into 96-well non-tissue culture treated round bottom plates (BD Falcon) and left untreated or stimulated for hours with 5ng/ml phorbol--myristate--acetate (PMA) and mm ionomycin in the presence of µg/mlbrefeldin A (Sigma) and monensin (BD). For live cell barcoding cells were transferred into V-bottom plates (Costar) washed in cold FACS buffer (PBS + % FCS + mm EDTA +.5% sodium azide) and incubated for 5 minutes at 7 C with a unique combination of metallabeled anti-human CD5 antibodies. Cells were then washed twice with ice-cold FACS buffer and Live/Dead stained with µm Cisplatin-Pt-98 (Fluidigm) for minutes at room temperature. Cells were washed and surface proteins were stained with antibodies at 7 C for 5 minutes and for an additional minutes at C. Cells were washed with FACS buffer and in order to perform intracellular staining, some samples were permeabilized using cytofix/cytoperm-buffer (BD) for minutes on ice and stained with an intracellular antibody cocktail page of 6 Nature Medicine: doi:.8/nm.66

3 Krieg et al. - HighDim analysis predicts response to PD- therapy supplementary data (Supplementary Table ) for min on ice. Finally, cells were incubated over night with 5nM iridium intercalator (Fluidigm) to label cellular DNA. Subsequently, cells were washed with PBS and with distilled water. Mass cytometry acquisition was performed on a CyTOF. (Helios) mass cytometer (Fluidigm). Antibody Conjugation Purified antibodies lacking carrier proteins were purchased from the companies listed in Supplementary Table. Antibody conjugation was performed using a metal-labeling kit (Fluidigm). CyTOF Data Analysis CyTOF data came from separate measurements (dataset and dataset ). In each measurement baseline (before treatment) and time point (after weeks of treatment) samples were stained separately resulting in experimental batches (dataset before, dataset after, dataset before, dataset after). In each batch, HD, NR and R samples were measured (Supplementary Figure ). We approached the dataset unbiased, meaning we did not expect a specific pattern. We mixed baseline and time point samples with the idea that prognostic markers that are identified at weeks may be useful for patient monitoring or possibly for the prediction of other endpoints such as overall response. Thus, the mathematical algorithm behind our mixed approach estimates the correlation of patients that enter with more than one sample and the algorithm is aware of baseline and time point samples. All analyses on CyTOF data were performed after arcsinh (with cofactor equal to 5) transformation of marker expression. In the following, we developed a custom R workflow in order to discover different biomarkers when comparing marker expressions between responders and nonresponders ( Further details of the majority of the pipeline, including many additional visualizations, optional analyses and R code, are published as a workflow article. All markers were included in the analysis and samples with less than 5 cells were excluded. This cutoff was used to balance the identification of relatively page of 6 Nature Medicine: doi:.8/nm.66

4 Krieg et al. - HighDim analysis predicts response to PD- therapy supplementary data rare population while retaining the identities of the main populations. Differential marker expression analysis was performed by fitting linear mixed models (LMM) using the lme R package. Here, marker expression either represents the global values (aggregated over all cells for a sample) or subpopulation-specific (aggregated over all cells in a cluster). The median marker expression is a response variable (y) and the explanatory variables include experimental group response (non-responder, responder or healthy donor) as a fixed effect. To account for any batch effects among samples, we use each individual experiment as an additional fixed effect (batch). We account for the fact that samples are paired (same sample measured before and after therapy) by introducing the patient ID as a random effect. To test for differences between responders and non-responders, we used the generalized linear hypothesis (glht) function from the multcomp R package to test for the four following contrasts: () the difference in marker expression between responders and non-responders before therapy, () differences after therapy, () overall differences in both combined and () an interaction that is comparing differences before and after therapy. Except for functional components (Figure ), we noted that in almost all cases that therapy did not have an impact on the observed significant differences. Based on this observation and in order to gain power, we report results of the overall differences between responders and nonresponders. To adjust for multiple comparisons, we adjusted the resulting p- values using the Benjamini Hochberg procedure. Differential marker expression is visualized using heatmaps as the change between responders and non-responders for significant markers (adjusted p- value <.). Colours represent normalized median marker expressions to mean of and standard deviation of. To rank markers according to their importance, we used the feature-scoring algorithm based on principal component analysis (PCA) from Levine et al., which identifies the non-redundant markers in each patient, while capturing the overall diversity. Top scoring (Levine PCA score averaged across samples) markers were used for subsequent clustering and dimension reduction analysis. In order to cluster single cell data, we used the SOM function from the FlowSOM R package 5 and ConsensusClusterPlus function from ConsensusClusterPlus R page of 6 Nature Medicine: doi:.8/nm.66

5 Krieg et al. - HighDim analysis predicts response to PD- therapy supplementary data package 6, a combination of methods that is amongst the best performing clustering approaches 7. In the first step, we used Flow SOM to assign cells to a x grid according to their similarity using the self organizing map (SOM) algorithm. In the second step, the resulting codes, vectors of marker expression representing the grid nodes, were clustered using ConsensusClusterPlus hierarchical clustering with average linkage. Since we knew the mapping between cells and nodes, we could reconstruct the final clustering for each individual cell. We applied ConsensusClusterPlus to cluster the codes into a range of clusters from to and to calculate a score (delta area), which we used to define the appropriate number of clusters present in the data based on the so called elbow criterion. For data visualization, we used tsne dimension reduction, to represent the annotated cell populations in a D map 8. Clusters were manually annotated based on the heatmaps with normalized to - median marker expression in each cluster and the afore mentioned tsne maps. To our knowledge there are no automatic annotation approaches thus cluster annotation remains a manual step in many approaches, e.g. Citrus, CellCnn. The recently proposed tool, called Marker Enrichment Modelling (MEM) 9, provides a consistent characterization of clusters, which consists of lists of markers that are positively and/or negatively enriched with respect to some predefined reference. Such naming again has to be manually interpreted in order to obtain meaningful names of cell types and thus stays subjective. A more detailed description and discussion of our clustering and labeling/annotating strategy, including its strengths and weaknesses can be found in our Bioconductor workflow. In order to analyze differences in relative cell population abundance (frequency) between responders and non-responders to anti-pd- therapy, we performed analysis analogous to differential marker expression analysis described above. Here, the response variable (y) was the number of cells in a given cluster in each sample, and instead of a LMM, a generalized linear mixed model (GLMM) with the binomial family was applied. page of 6 Nature Medicine: doi:.8/nm.66

6 Krieg et al. - HighDim analysis predicts response to PD- therapy supplementary data Cytokine analysis based on a bimatrix For the selected subpopulations, we investigated changes in cytokine production between responders and non-responders. Based on published cytokine analysis algorithms such as COMPASS, we exploited a new type of analysis based on a so-called bimatrix. Bimatrix is a binary matrix with rows representing cells and columns corresponding to the cytokines of interest where each entry encodes whether a cell is positive () or negative () for a given cytokine. Thresholds for defining the positive status of a cell were defined for each batch of data individually by investigating expression profiles in FlowJo using DMSO or a biological negative control. Subsequently, we performed two types of comparisons. First, the differential frequency analysis based on GLMM, which compare the frequencies of positive cells in responders and non-responders for each individual cytokine (Figure A and A). For the second analysis, we considered an entire cytokine set profile of each cell. Cells described by the bimatrix were clustered using the SOM method into 9 groups (7 times 7 grid) to generate profiles of the cytokine production, which we refer to as cytokine combination groups (CCGs) (Figure B and B), and the relative abundance of these profiles was compared between responders and non-responders using the GLMM approach described above (Figure D and D). CellCnn analysis We used default parameter settings to run CellCnn, including randomly splitting data into training and validation sets in order to train the model. As CellCnn does not provide any measure for the significance of identified filters, we have used our GLMM approach (a model with observation-level random effects or OLRE to correctly model over-dispersed binomial data) to test whether the identified cell population is significantly over-represented in responders (Supplementary Figure ). Of note, the p-values obtained with this approach do not account for the selection step (only the selected population is tested). However, such p- values can be still informative of the magnitude of observed differences. Validation by flow cytometry After thawing, cell suspensions were stained in staining buffer (PBS, 5mM EDTA, page 5 of 6 Nature Medicine: doi:.8/nm.66

7 Krieg et al. - HighDim analysis predicts response to PD- therapy supplementary data.5% BSA) containing Fc-block (Miltenyi) with the following antibody cocktail (clones in brackets, all from Biolegend until noted otherwise): CDb- BrilliantViolett (BV) (ICRF), CD-PE (HCD), HLA-DR-FITC (L), CD-BV7 (OKT), CD-BV65 (WM5), and Live/dead-stain-NearInfraRed. CD56-Pe-Cy7 (NCAM) and CDc-AlexaFluor7 (B-Ly6) were both from BD Biosciences, CD6-APC (G8) from ThermoFischer and CD5RO-ECD (HLDHLD89(H), from Beckman Coulter. The frequencies of two cell populations, which were CD + T cells and CD + CD6 - HLA-DR + monocytes, were extracted from the three groups. For statistical testing, we applied a generalized linear model (GLM) with beta family, using the glmmadmb R package, where the response y is an relative abundance (proportion) of a cell population in the sample. The contrast for the comparison between responders and nonresponders was tested using the glht function and a Benjamini Hochberg procedure was applied to correct the resulting p-values for multiple-testing. Correlation of PFS with monocyte frequency In order to visualize and quantify the difference in PFS associated with classical monocyte frequencies at baseline from both cohorts, we removed batch effects and calculated the optimal cutoff point in the classical monocytes frequency, which best dichotomizes responders from non-responders. The calculations were performed in R, using the OptimalCutpoints package and the Youden method. To compute the cumulative hazard function we used the previously calculated cutoff of 9.9% to create the groups. This was performed in R using the survfit function of the survival package and the ggsurvplot function of the survminer package. Patient data and analysis Standard clinical parameters (k=5) were collected at baseline for the two cohorts (n=5). To assess the potential correlation between progression-free survival (PFS) and any of the clinical variables plus the frequency of classical monocytes, we performed a Cox proportional-hazards regression. Gender, previous treatment, mutation status, metastasis localization and primary tumor ulceration were considered as binary variables, melanoma-staging parameters page 6 of 6 Nature Medicine: doi:.8/nm.66

8 Krieg et al. - HighDim analysis predicts response to PD- therapy supplementary data were considered as ordinal variables and the rest as continuous variables (Supplementary Figure 6). To account for the batch effects (CyTOF dataset, CyTOF dataset and FACS) on the measured frequencies of classical monocytes we normalized them per batch to mean of zero and standard deviation of one. Candidate prognostic factors with p-values smaller than.5 in the univariate analysis (Supplementary Figure 7A) were then included into the multivariate model (Supplementary Figure 7B). Of note, p-values in the multivariate model do not account for pre-selection from the univariate step. Calculations were performed in R using the coxph() function from the survival package and the forest plot was generated using the forestplot() function from the rmeta package. The same 5 parameters were tested for association with response (NR versus R) using linear models (LM) for continuous parameters and generalized linear models (GLM) for the binary parameters. In both cases, in regression models, the clinical parameters were treated as dependent variables y, and response (NR, R) was treated as the explanatory variable. To adjust for multiple comparisons, we adjusted the resulting p-values using the Benjamini Hochberg procedure. Immunohistology Immunohistology was done according to published protocols. The assessment of tumor infiltrating cell was performed on formalin-fixed paraffin-embedded tumor samples, sourced from patients previously included in our cohorts (5 responders and 8 non-responders). Samples with a collection date the closest to the treatment initiation date were included (mean=.days, median=days, range=--5days). Tumors were fixed in formalin and subsequently embedded in paraffin. For immunohistochemistry staining, sections were deparaffinized, rehydrated and pretreated with EDTA (Sigma-Aldrich), TSR9. or proteinase-k (Sigma-Aldrich) before performing staining with one of the following primary antibodies (all antibodies from DAKO until stated otherwise): anti-human CD (clone F7..8, :5), rabbit anti-human CD (clone EPR6855, :, Abcam), mouse anti-human CD8 (clone M7, :5), mouse anti-human CD68 (clone M8, :), mouse anti-human CD6 (clone D6, Abcam), or rabbit anti-human PD-L (clone 68, Cell Signaling). As as secondary reagent for the mouse antibodies AP K55 anti rabbit IgG-biotin for the rabbit page 7 of 6 Nature Medicine: doi:.8/nm.66

9 Krieg et al. - HighDim analysis predicts response to PD- therapy supplementary data antibodies (Vector Labs) were used. For the anti-pd-l stain a blocking step with goat serum was added before the secondary antibody. Visualization was obtained with Alkaline Phosphatase/Red reagent, chromogen or AEC (all Dako). After counterstaining with haematoxylin, sections were dehydrated and prepared for visualization by mounting with mounting medium Eukitt (Sigma- Aldrich). page 8 of 6 Nature Medicine: doi:.8/nm.66

10 Krieg et al. - HighDim analysis predicts response to PD- therapy supplementary data SUPPLEMENTARY TABLES AND FIGURES Supplementary Table. Characteristics of blood samples from melanoma patients and healthy donors used for the biomarker discovery study. Healthy donors Melanoma patients Responders Non-responders Time point Time point Total N dataset 5 5 N dataset 5 5 N TOTAL Age in years mean (range) 6. (6-7) Sex male/female 6/ Before therapy After therapy N dataset 5 5 N dataset 6 6 N TOTAL Age in years mean (range) 6. (-8) Sex male/female 9/ N dataset 5 5 N dataset N TOTAL Age in years mean (range) 57.8 (5-75) Sex male/female 5/ Supplementary Table. Staining panels for Mass Cytometry Data Sets. Panel (T cell phenotype) Panel (cytokines) Panel (myeloid) Mass Antigen Clone Distributor Mass Antigen Clone Distributor Mass Antigen Clone Distributor 89 CD5 HI Fluidigm 89 CD5 HI Fluidigm 89 CD5 HI Fluidigm CD5 HI BioLegend CD5 HI BioLegend CD5 HI BioLegend 5 CD5 HI BioLegend 5 CD5 HI BioLegend 5 CD5 HI BioLegend 6 CD5 HI BioLegend 6 CD5 HI BioLegend 6 CD5 HI BioLegend 8 CD5 HI BioLegend 8 CD5 HI BioLegend 8 CD5 HI BioLegend CD5 HI BioLegend CD5 HI BioLegend CD5 HI BioLegend CCR6 GE/A9 Fluidigm CD5RA HI Fluidigm CD9 HIB9 Fluidigm CDa HI Fluidigm IL- MP-5D Fluidigm 6 CD6. Fluidigm CD5RA HI Fluidigm 5 CD RPA-T/OKT Fluidigm 7 CD A BioLegend CCR5 NP-6G/J8F BioLegend 6 CD8a RPA-T8 Fluidigm 8 CD 58 Fluidigm 5 CD RPA-T/OKT Fluidigm 8 IL-7A BL68 Fluidigm 9 CD M8 BioLegend 6 CD8a RPA-T8 Fluidigm 9 CD5 A Fluidigm 5 CD6 VI-PL Fluidigm 9 CD5 A Fluidigm 5 TCRgd F Fluidigm 5 CD 6H6 Fluidigm 5 TCRgd F Fluidigm 55 CD7 L8 Fluidigm 5 CD66b 8H Fluidigm 5 CD6L DREG56 Fluidigm 56 IL- JES-5A BioLegend 5 CD6L DREG-56 Fluidigm 5 LAG- 7B Enzo 58 IL- MQ-7H Fluidigm 5 ICAM- C R&D 55 CD7 L8 Fluidigm 59 GM-CSF BVD-C Fluidigm 55 CDc L6 BioLegend 56 CXCR G5H7 Fluidigm 6 CD8 CD8. Fluidigm 56 CD86 IT. Fluidigm 58 CCR 5 Fluidigm 6 CTLA D Fluidigm 6 CD M5E Fluidigm 6 CD8 CD8. Fluidigm 6 CD69 FN5 Fluidigm 6 CDc N8 Fluidigm 6 CTLA D Fluidigm 6 CD5RO UCHL Fluidigm 6 CD7 CD7-6B7 BioLegend 6 CD69 FN5 Fluidigm 65 IFN-g B7 Fluidigm 65 CD6 G8 Fluidigm 6 CD95 DX5 Fluidigm 66 IL- JES-97 Fluidigm 66 CD9 DCN6 BD 65 CD5RO UCHL Fluidigm 67 CCR7 GH7 Fluidigm 67 CD8 HIT Fluidigm 66 BTLA J68-5 BD 68 TNF-a MAb BioLegend 69 CD WM5 Fluidigm 67 CCR7 GH7 Fluidigm 69 CD9 HIB9 Fluidigm 7 CD SP. Fluidigm 69 CD9 HIB9 Fluidigm 7 CD SP. Fluidigm 7 CD56 NCAM6. BD 7 CD SP. Fluidigm 7 Granzyme-B GB Fluidigm 7 HLA-DR L Fluidigm 7 Granzyme-B GB Fluidigm 7 CD56 NCAM6. BD 75 CD7 (PDL) 9E.A Fluidigm 7 CD57 hcd57 Fluidigm 75 PD- EH.H7 Fluidigm 9 CDb ICRF Fluidigm 7 CD56 NCAM6. BD 76 CD7 A9D5 Fluidigm 7 HLA-DR L Fluidigm 75 PD- EH.H7 Fluidigm 76 CD7 A9D5 Fluidigm 9 CD6 G8 Fluidigm page 9 of 6 Nature Medicine: doi:.8/nm.66

11 Krieg et al. - HighDim analysis predicts response to PD- therapy supplementary data Supplementary Table Characteristics of melanoma patients and healthy donors used for the validation study. Time point N TOTAL Age in years mean (range) Sex male/female Healthy donors 6. (6-9) 7/7 Before therapy 5 Melanoma patients N TOTAL Age in years mean (range) Sex male/female N TOTAL Age in years mean (range) Sex male/female Responders Dataset (measurement )! Non-responders Before treatment before (baseline)! before x! x! x6! x6! Treated! Staining! Staining! Dataset (measurement )! after Treated! B Treated! Staining! Staining! Staining! Staining! Dataset (measurement )! After treatment! after (time point)! Dataset (measurement )! A 58.9 (-9) 9/6 Before treatment After treatment! 6 (baseline)! (time point)! 6.9 (7-89) Staining! Staining! 8/8 x! x! x6! x6! Treated! Supplementary Figure. Experimental design for the discovery cohort using CyTOF. Experimental setup for the processing of frozen PBMC from matched samples before and after PD- immunotherapy. (A) Total samples n=6 were distributed over datasets. melanoma patients before (R= and NR=9) and after ( weeks treatment) treatment initiation and healthy controls using metal-labeled antibodies and (B) subsequent processing of samples from A for the acquisition by mass cytometry and final bioinformatics analysis. page of 6 Nature Medicine: doi:.8/nm.66

12 Krieg et al. - HighDim analysis predicts response to PD- therapy supplementary data Supplementary Figure. Simultaneous detection of T cell differentiation and activation markers in blood. PBMCs from 5 healthy donors and melanoma patients were barcoded, stained with a panel of antibodies and analyzed by mass cytometry. Biaxial mass cytometry plots show the staining quality by gating on combined healthy samples from a respective positive and negative cell population of the shown differentiation and activation marker. Each plot shows a representation of four independent experiments. page of 6 Nature Medicine: doi:.8/nm.66

13 Krieg et al. - HighDim analysis predicts response to PD- therapy supplementary data A CD6 CD CD Percentages CD68 CD8 PD L NR R NR R NR R Staining intensity B CD R CD NR blood monocyte frequncy R monos R CD CD staining intensity in tumor blood monocyte frequency (%) NR monos NR CD CD staining intensity in tumor PD-L R PD-L NR blood monocyte frequency (%) R monos R PD-L PD-L staining intensity in tumor blood monocyte frequency (%) NR monos NR PD-L PD-L staining intensity in tumor Supplementary Figure. Immunohistochemical intensity scores of lymphocytes and monocytes markers in FFPE melanoma tumor samples. (A) Matched tumors to liquid biposies from 5 non-responders (NR) and 8 responders (R) previously analysed in the discovery or the validation cohort were stained for CD6, CD68, CD, CD8, CD and PD-L (sampling date is in a range of 5 days from the start of treatment date). Numbers next to the blue scale indicate staining intensities (=null, =-%, =-%, = -6%, = >6%). (B) Paired comparison of myeloid blood cell frequencies in responders (R, green) and nonresponders (NR, red) with CD and PD-L staining intensities in tumor at baseline. page of 6 Nature Medicine: doi:.8/nm.66

14 Krieg et al. - HighDim analysis predicts response to PD- therapy supplementary data page of 6 A Before therapy Legend: CD57 CD8 CD7 Granzyme B CD95 HLA DR CXCR CCR CTLA BTLA LAG CDa CD69 PD CCR5 CCR6 adjp_nrvsr_base merged 5 clusters adjusted p-value CDb HLA DR ICAM CD CD8 CD86 CD CD7_PDL CD6 CD56 CD6L CDc CD9 CD CDc CD CD5 CD CD6 CD6 CD adjp_nrvsr_base cluster_merging CD+_monos CDlo_mono DC pdc drop cluster in out apvs (,.5] (.5,.] (.,] CDb HLA DR ICAM CD CD8 CD86 CD CD7_PDL CD6 CD56 CD6L CDc CD9 CD CDc CD CD5 CD CD6 CD6 CD adjp_nrvsr_base cluster_merging CD+_monos CDlo_mono DC pdc drop cluster in out apvs up(,.] up(.,.5] up(.5,.] up(.,] down(,.] down(.,.5] down(.5,.] down(.,] normalized median expression Nature Medicine: doi:.8/nm.66

15 CD57 CD57 CD6L CD6L CCR7 CCR7 6 Krieg et al. - HighDim analysis predicts response to PD- therapy supplementary data CD8 CD8 B After therapy CD7 CD7 merged 5 clusters CD7 CD7 Granzyme B Granzyme B CD57 CDb CD95 CD95 HLA DR CD8 CD5RO CD5RO ICAM CD7 HLA DR HLA DR CD Supplementary Figure. Defining CD8 + T cells subpopulations with over-clustering. CDb HLA DR ICAM CD Annotation of unmerged clusters obtained with FlowSOM on CD8 + T cells before (A) and after (B) therapy. Shown are two representative heatmaps of - normalized median marker expression in each cluster (HD n=, patients n=). The bar on the right indicates adjusted p- values from cluster frequency comparisons between R and NR and the direction of changes (upblue, down-red in R versus NR). The second most left bar shows clustering into 5 groups obtained with consensus step. Bar on the most left shows how the 5 clusters were annotated. All p-values were calculated using two-sided t-tests and were corrected for the multiple comparison using the Benjamini-Hochberg adjustment. CD5RA CD5RA Granzyme B CD8 CD8 CXCR CXCR CD86 CD86 CD95 CCR CCR CD CD CTLA CTLA HLA DR CD7_PDL CD7_PDL BTLA BTLA CXCR CD6 CD6 LAG LAG CD56 CD56 CDa CDa CCR CD6L CD6L CD69 CD69 CTLA CDc CDc PD PD BTLA CD9 CD9 CCR5 CCR5 CD CD CD5 CD5 LAG CDc CDc CCR6 CCR6 CDa CD CD adjp_nrvsr_base adjp_nrvsr_base CD69 CD5 CD CD CD cluster_merging naive CM EM cluster_merging TE Legend: cluster naive CM cluster name cluster_merging EM naive TE cluster CM EM 5 in TE normalized.75 median.5 5 expression in.5 out out.5 apvs adjusted.75 p-value (,.5].5 (.5,.].5 (.,] apvs (,.5] (.5,.] (.,] PD CD6 CD6 CCR5 CD6 CD6 CCR6 CD CD adjp_nrvsr_base adjp_nrvsr_base adjusted adjp_nrvsr_tx p-value cluster_merging cluster_merging 6 CD+_monos 6 97 cluster_merging CDlo_mono naive 7 DC CM CD+_monos 7 pdc EM CDlo_mono 5 drop TE DC cluster 6 pdc 6 drop 7 cluster in out apvs up(,.] up(.,.5] 87 6 in up(.5,.] in up(.,] down(.,] 9 normalized median expression 96 6 out out apvs (,.5] 5 85 apvs (.5,.] (.,] up(,.] 9 up(.,.5] up(.5,.] 9 up(.,] down(,.] down(.,.5] down(.5,.] down(.,] page of 6 Nature Medicine: doi:.8/nm.66

16 Krieg et al. - HighDim analysis predicts response to PD- therapy supplementary data Supplementary Figure 5. Simultaneous detection of TH and CTL profiles in human blood. PBMC from melanoma patients were stimulated for hours with PMA/Ionomycin in the presence of brefeldin A. Two-dimensional mass cytometry plots show one out of four independent experiments. Supplementary Figure 6. Characterization of the circulating myeloid compartment in the blood of melanoma patients. Shown are dot plots from mass cytometry staining panels on PBMC samples. Gates are on all live cells, or CD+, or CD- CD9- subpopulations. Data is representative of one out of four independent experiments. page 5 of 6 Nature Medicine: doi:.8/nm.66

17 Krieg et al. - HighDim analysis predicts response to PD- therapy supplementary data Frequency (%) Frequency in PBMC Frequency (%) (%) T_cells B_cells CD CD+ NK_cells cdc pdc e-. 7.e e T_cells B_cells CD CD+ NK_cells cdc pdc e e e- T_cells B_cells 9 CD CD+ NK_cells cdc pdc HD. 7 8 NR R T_cells 6 B_cells CD CD+ NK_cells 6 cdc pdc HD data dataset. 7 8 NR data9 dataset R data9 7.5 Frequency (%) Frequency (%) Before After Supplementary Figure 7. Comparison of frequencies in cellular data sub-populations using the myeloid panel. Cluster frequencies in healthy donors (HD, black), non-responders (NR, pink) and responders (R, green) in dataset and. Asterisks indicate the significance level of differences in cell frequencies between NR and R before and after treatment (numbers show adjusted p-values, HD n=, NR n=8, R n=). Boxplots represent the interquartile range (IQR) with the horizontal line indicating the median. Whiskers extend to the farthest data point within a maximum of.5 IQR. All p-values were calculated using two-sided t-tests and were corrected for the multiple comparison using the Benjamini-Hochberg adjustment. page 6 of 6 Nature Medicine: doi:.8/nm.66

18 Krieg et al. - HighDim analysis predicts response to PD- therapy supplementary data CD CD8 6 R=.9 R=.87 Frequency of IFN-γ + (%) 8 6 Frequency of CD + myeloid cells (%) R=.9 R=.87 base HD base NR NR base R tx CD7_PDL Median PD-L expression on all myeloid cells Supplementary Figure 8. Relationship between the frequency of IFN-γ-producing T cells with myeloid cell frequency and median PD-L expression on myeloid cells. Presented are results for after-treatment samples from dataset (n=5). R indicates the Spearman correlation. page 7 of 6 Nature Medicine: doi:.8/nm.66

19 67 8 Krieg et al. - HighDim analysis predicts response to PD- therapy supplementary 79data 9 Complex heatmap supporting figure D CD+ cluster Cluster frequency (%) clusters 6 CDb CDb HLA DR ICAM CD CD8 CD86 CD Supplementary Figure 9. In depth analysis of the CD + myeloid compartment before up(,.] 5 down(,.] therapy. Initially, over-clustering into groups was performed with FlowSOM on all cells from 57 HD, NR and R, before therapy and under therapy for each dataset ( and ) separately. Using 7 consensus clustering, clusters were merged into groups, which were then 8 manually 6 annotated. Shown are marker profiles (median marker expression normalized to - range, HD CDb HLA DR HLA DR ICAM ICAM CD CD CD8 Base CD8 CD86 CD86 CD CD n=, patients n=) for clusters (at the resolution) that correspond to CD + cells (red bar on the left). Column on the right shows adjusted p-values and direction of change (up or down) of individual clusters when comparing R to NR before therapy (base). CD7_PDL CD7_PDL cluster CD7_PDL CD6 CD6 CD6 Cluster frequency (%) CD56 CD56 CD56 6 CD6L CD6L CD6L CDc CDc CDc HD NR R CD9 CD9 CD9 CD CD CD CDc CDc Therapy CDc CD CD CD CD5 CD cluster CD5 CD CD CD CD6 CD6 CD6 CD6 CD6 CD6 CD CD CD adjp_nrvsr_base adjp_nrvsr_tx adjp_nrvsr_base adjusted p-value base adjp_nrvsr_base adjp_nrvsr_tx in in 85 out 7 9 normalized median expression.5 out out apvs up(,.] up(.,.5] up(.5,.] 6 98 apvs.5 up(.,] adjusted down(,.] p-value apvsdown(.,.5] up(,.] down(.5,.] up(.,.5] down(.,] up(.5,.] cluster_merging up(.,] 568 CD+_monos down(,.] 5569 CDlo_mono down(.,.5] DC down(.5,.] 7 8 pdc down(.,] 6 57 drop cluster in out apvs adjusted p-value therapy up(.,.5] up(.5,.] up(.,] down(.,.5] down(.5,.] down(.,] page 8 of 6 Nature Medicine: doi:.8/nm.66

20 Krieg et al. - HighDim analysis predicts response to PD- therapy supplementary data page 9 of 6 Supplementary Figure. Identification of a monocyte signature by CellCnn. Frequency of cells discovered using CellCnn in non-responders (NR) and responders (R) from dataset (left panel) and relative marker distributions, shown as scaled histograms of arcsinh-transformed marker expression, for all cells (blue) and the detected population (red) (right panel). Supplementary Figure. Back projection of cells identified using CellCnn into tsne. Shown are tsne plots corresponding to Figure A. Cells identified by CellCnn are marked with black circles (arrow). Filtering using CellCnn *** CD5 KS=. CD9 KS=.9 CD6 KS=.5 CD KS=.7 CD KS=.8 CD KS=.58 CD6 KS=. CD KS=. CD66b KS=.58 CD6L KS=.9 ICAM- KS=.8 CDc KS=.9 CD86 KS=.6 CD KS=.85 CDc KS=.8 CD7 KS=.6 CD6 KS=.6 CD9 KS=.8 CD8 KS=.7 CD KS=.5 6 CD KS=. CD56 KS=. HLA-DR KS=.5 PD-L KS=.5 CDb KS=.8 NR R t SNE t SNE T_cells B_cells CD CD+ NK_cells cdc pdc NR R tsne tsne Nature Medicine: doi:.8/nm.66

21 Krieg et al. - HighDim analysis predicts response to PD- therapy supplementary data CXCL P=. FDR=.59 5 counts per million 5 HD NR R Supplementary Figure. CXCL expression by RNA-seq. Box plots showing expression (in counts per million) of CXCL measured with RNA-seq in CD + HLA-DR + monocytes in HD, NR and R at baseline. Shown are the p-value (P) and the false discovery rate (FDR) from the comparison between NR and R (NRn=R n=hdn=). page of 6 Nature Medicine: doi:.8/nm.66

22 Krieg et al. - HighDim analysis predicts response to PD- therapy supplementary data A Number of model features B Model Cross Validation Error Rate Cross Validation Error Rate Feature False Discovery Rate cv.min cv.se cv.fdr.constrained R NR R NR. cluster 999 abundance cluster 999 abundance Log scale Regularization Threshold C abundance D Myeloid T cell Yb_GranzymeB 69Tm_CD9 9Bi_CD6 65Ho_CD5RO 7Yb_CD57 76Yb_CD7 5Eu_CD6L 6Gd_CD8 6Nd_CD8a 5Nd_CD.CD 55Gd_CD7 7Yb_CD 7Yb_HLA DR 999 (%) 999 (.5%) 5 Supplementary Figure. Citrus analysis of the T cell panel. Using a predictive model (pamr) Citrus identified clusters for which abundance was the best predictor of the response to the PD- treatment. (A) Cross validation results presenting estimated error rates of models considered by Citrus. Reported are results for the minimum error rate model (cv.min). (B) Cell abundance in clusters identified by Citrus as associated with the response, stratified by response (responder R and non-responder NR). (C) Clustering hierarchy of clusters generated by Citrus that contain at least 5% of total cells. (D) Heatmap representing overall marker expression in clusters identified by Citrus as associated with response. page of 6 Nature Medicine: doi:.8/nm.66

23 Krieg et al. - HighDim analysis predicts response to PD- therapy supplementary data A B R cluster 8 abundance Number of model features NR Model Cross Validation Error Rate Cross Validation Error Rate Feature False Discovery Rate cv.min cv.se cv.fdr.constrained R NR R NR R cluster 8 abundance cluster 87 abundance cluster 8 abundance NR C Regularization Threshold abundance. Log scale D 7987 NK/T cell NK/T cell NK/T cell Myeloid (%) 87 (.7%) 8 (.8%) 8 (65.%) CDc CD6L CD56 CD6 CD7_PDL CD CD7 CD86 CD8 CD ICAM HLA DR CDb Supplementary Figure. Citrus analysis of the myeloid cell-enriched (CD-CD9-) panel. Using a predictive model (pamr) Citrus identified clusters for which abundance was the best predictor of the response to the PD- treatment. (A) Cross validation results presenting estimated error rates of models considered by Citrus. Reported are results for the minimum error rate model (cv.min). (B) Cell abundance in clusters identified by Citrus as associated with the response, stratified by response (responder R and non-responder NR). (C) Clustering hierarchy of clusters generated by Citrus that contain at least 5% of total cells. (D) Heatmap representing overall marker expression in clusters identified by Citrus as associated with response. page of 6 Nature Medicine: doi:.8/nm.66

24 Krieg et al. - HighDim analysis predicts response to PD- therapy supplementary data Exclude! Supplementary Figure 5. FACS validation panel. PBMC from an independent, randomized, blinded patient cohort were stained for CD, CD, CDb, CD, CD9, CD6, CD, CD5RO, CD56, and HLA-DR, acquired and analyzed using the above gating strategy. Note the position of the lymphocytes and monocytes gate (arrow). page of 6 Nature Medicine: doi:.8/nm.66

25 Krieg et al. - HighDim analysis predicts response to PD- therapy supplementary data Discovery cohort Validation cohort PATIENT CHARACTERISTICS N % N % Age < years old.%.9% -55 years old 7 5.%.% 56-7 years old 7 5.% 6 9.% > 7 years old 6.% 5.5% Gender Male 65.% 7 5.8% Female 7 5.% 5.% Progression-free survival (PFS) < months 6.% 8.7% -9 months 5 5.%.9% > 9 months 9 5.% 6 9.% Lactate dehydrogenase (LDH) < 8 U/l 8 9.% % 8 U/l.%.% S <. μg/l 9 5.% %. μg/l 55.%.% Neutrophils (ANC) < 8 G/l 8 9.% 96.8% 8 G/l.%.% Lymphocytes (ALC) <.5 G/l 55.% 6.5%.5 G/l 9 5.% 5.5% Leukocytes < 9.6 G/l 7 85.% 96.8% 9.6 G/l 5.%.% Monocytes <.95 G/l 7 85.% 96.8%.95 G/l 5.%.% Thrombocytes < G/l 5.%.% - G/l 8 9.% 8 9.% > G/l 5.% 9.7% Eosinophils <.7 G/l.%.%.7 G/l 6 8.%.% Basophils <.5 G/l.%.%.5 G/l.%.% Haemoglobin < g/l 55.% 5 8.% g/l 9 5.% 6 5.6% Hematocrit <. l/l 7 5.% 5.5%. l/l 65.% 6.5% Erythrocytes <. T/l 6.% 7.6%. T/l 7.% 77.% Mean corpuscular volume (MCV) < 8 fl.%.% 8 fl.% 96.8% Mean corpuscular haemoglobin (MCH) < pg.% 9 9.5% pg.% 6.5% Mean corpuscular haemoglobin concentration (MCHC) < g/l 5.%.% g/l 9 95.% 96.8% Red blood cell distribution width (RDW) <.8 % 85.7% 78.6%.8 %.% 6.% Immature granulocytes absolute <. G/l % 6 8.9%. G/l.% 5 6.% Immature granulocytes <.5 % % 8 9.%.5 %.5% 9.7% Sodium < 6 mmol/l 5.%.% 6 mmol/l 7 85.% 96.8% Potassium <. mmol/l.%.% mmol/l 7 9.% % >.5 mmol/l 5.6%.% Urea < 7. mmol/l % 7 87.% 7. mmol/l.%.9% Creatinin < 6 μmol/l.% 8 6.7% 6-6 μmol/l 6 8.% 8 6.% > 6 μmol/l.%.% Estimated glomerular filtration rate (egfr) < 9 ml/min 9 5.% % 9 ml/min 55.%.% Bilirubin < μmol/l 6 9.% 7 9.% μmol/l 5.9% 6.9% Protein < 66 g/l 7.%.8% 66 g/l 9.9% 5 88.% Albumin < g/l.%.% g/l 76.9% 88.% Aspartate aminotransferase (AST) < 5 U/l 6 9.% 5 9.6% 5 U/l 5.9% 7.% Alanine aminotransferase (ALT) < 5 U/l 8 9.7% 96.8% 5 U/l 5.%.% Gamma-glutamyl transpeptidase (GGT) < 6 U/l.% 5 8.% 6 U/l.% 5 6.7% Alkaline phosphatase < U/l.5% 6.5% - 9 U/l 6 8.% 7 87.% > 9 U/l 5.% 6.5% C-reactive protein (CRP) < 5 mg/l 66.7% % 5 mg/l 6.% 9.% Thyroid-stimulating hormone (TSH) <.8 mg/l 8.% 6 8.9%.8 mg/l 8.8% 5 6.% Supplementary Figure 6. Baseline clinical parameters of patients. Clinical parameters of the patients included in the discovery mass cytometry (N=) and FACS validation (N=) approaches. Parameters were categorized according to standard clinical cutoffs (e.g. LDH > 8U/l). The percentages are calculated on the total number of patients in the cohort and therefore account for missing values. page of 6 Nature Medicine: doi:.8/nm.66

26 Krieg et al. - HighDim analysis predicts response to PD- therapy supplementary data A variables Thrombocytes Classical monocytes (CD+CD6 ) Lactate dehydrogenase (LDH) Liver metastasis Alkaline phosphatase Immature granulocytes (%) M classification = Potassium Hematocrit Albumin Melanoma stage IV N classification = Hemoglobin Aspartate aminotransferase Brain metastasis Sex (Male) Erythrocytes Gamma glutamyl transpeptidase Tyroid stimulating hormone Previous targeted therapy ckit mutated N classification = Monocytes N classification = C reactive protein S Previous chemo treatment Previous radio treatment Basophils Bone metastasis Lymphocytes (ALC) Mean corpuscular hemoglobin Estimated glomerular filtration rate Lung metastasis Ulcerated primary tumor BRAF mutated Mean corpuscular hemoglobin conc. Total protein Alanine aminotransferase NRAS mutated Primary tumor classification = Eosinophils Bilirubin Urea Red cell distribution width Age at sampling Primary tumor classification = Number of days since last ipi treatment Sodium Leukocytes Previous ipi treatment Creatinine ANC/ALC Mean corpuscular volume Neutrophils (ANC) Primary tumor classification = Nodal metastatic mass = macrometastasis Nodal metastatic mass = in transit metastasis HRate LCL(95%).e.e+.e.9e.5e.e.7e+.7e+.5e+.8e.69e+.9e+.69e.7e.8e+.e+.6e+.96e 7.5e.96e 6.5e.89e+ 6.e.e+ 9.87e 8.79e.58e 9.88e.e+.e+ 7.96e.e.5e 8.79e.7e+ 9.e.5e 7.e.5e 6.78e.75e+.86e+.57e.7e 6.e.57e.9e+.e.5e.9e 9.e.7e.68e.75e.e.5e+ Inf Inf UCL(95%) Inf Inf Pvalue 9.8e 5.8e.e.59e 6.8e.9e.7e.6e 6.e 7.67e.e.7e.e.e.e.7e.6e.85e.89e.9e.9e.e.7e.87e.7e.e.7e.8e.9e.85e.99e 5.e 5.6e 5.88e 6.e 6.5e 6.5e 6.59e 6.7e 6.85e 7.58e 7.6e 7.67e 7.9e 8.8e 8.5e 8.59e 8.7e 9.e 9.e 9.55e 9.65e 9.8e 9.87e 9.9e 9.9e 9.99e 9.99e B Hazard Rate variables Classical monocytes (CD+CD6 ) Immature granulocytes (%) Lactate dehydrogenase (LDH) M classification = Alkaline phosphatase Thrombocytes Potassium Liver metastasis HRate LCL(95%) UCL(95%) Pvalue Hazard Rate Supplementary Figure 7. Hazard rates according to baseline characteristics of patients included the discovery mass cytometry and validation conventional flow cytometry approaches. (A) Univariate Cox regression analysis of progression-free survival (PFS). (B) Multivariate Cox regression analysis included variables with p-values <.5 from the univariate analysis. The columns next to the variables in the table show the coefficients Hazard Rate (HRate), the lower (LCL) and upper (UCL) 95% confidence intervals, and the associated p-value (Pvalue) derived from the Cox regression. Missing values for baseline characteristics were not inputed. Red squares represent hazard rates. Bars represent 95% confidence interval (n=5). page 5 of 6 Nature Medicine: doi:.8/nm.66

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