OS Multivariate analysis showing that markers significant during univariate analysis are not significant during multivariate analysis.

Experimental data userd in the article including Olink, CyTOF populational frequencies, Serology and TCRseq beta chain.

Supplemental Figure 1. A. Volcano plots showing differentially expressed proteins with P<0.05 between treatments.

Supplemental Figure 3. Graphical abstract. Top boxes show a simplified trial design including 3 treatments composed by the combination of BV with I, N and I+N. We investigated the differences between responders and non-responders using standardized assays through the CIMAC’s network (ELISA, Olink, CYTOF and TCRseq). Bottom boxes summarize the main findings associated with specific treatments and treatment associated response or resistance.

Supplemental Figure 2. Cellular marker dynamics in Hodgkin lymphoma during checkpoint blockade treatment. A. Heatmap showing markers and cell types identified through CyTOF (cytometry using time of flight) that significantly change over time. The color indicates the standardized Log2FC in protein expression (Z-Score), and the size of the circles indicate the percent of cells expressing the marker. A dendrogram of the markers sorted hierarchically is shown on the right side. B. Line plots highlight the temporal changes for each of the markers shown in A. The color separates the values per treatment. C. Heatmap showing markers and cell types significantly associated with R (Responder) and NR (Not responder). D. Regression lines for examples of markers and cell types shown in C, separating response (R) and non-response (NR).

Protein dynamics in HL during checkpoint blockade treatment. A, Overview of the clinical trial E4412 experimental design. Three treatment arms included: (i) BV + ipilimumab (I), (ii) BV + nivolumab (N), and (iii) BV + I + N, with participant number (n) indicated. B, Regression modeling strategy using mixed effect models applied to analyze independently four different assay methodologies. Each assay was modeled considering relevant clinical variables and adjusted for multiple testing using FDR correction. C, Summary heatmap showing the log2-fold change (log2FC) between time points and treatments. The changes with a positive log2FC over time in color blue are associated with a decrease over time and red with an increase over time. The −log10 (P value) is represented by the size of the circles, indicating statistical significance as the circles increase. D, Line and boxplot figures showing the changes in expression for markers increased posttreatment such as PDCD1, GMZA, PTN, CAIX, IL18, CD28, and markers decreased posttreatment such as CCL17, ANGPT2, IL13, and CXCL13. E, Summary heatmap of differential expression associated with response. The changes with a positive log2FC in color blue are associated with a lower expression in nonresponders and red with higher expression in nonresponders. F, Line and boxplot examples of significant (P < 0.05 & FDR < 0.05) proteins associated with response (blue) or nonresponse (red) over time.

Supplementary Figure 1. Available tumor tissue samples and blood samples.

AbstractPurpose:Identifying molecular and immune features to guide immune checkpoint inhibitor (ICI)-based regimens remains an unmet clinical need.Experimental Design:Tissue and longitudinal blood specimens from phase III trial S1400I in patients with metastatic squamous non–small cell carcinoma (SqNSCLC) treated with nivolumab monotherapy (nivo) or nivolumab plus ipilimumab (nivo+ipi) were subjected to multi-omics analyses including multiplex immunofluorescence (mIF), nCounter PanCancer Immune Profiling Panel, whole-exome sequencing, and Olink.Results:Higher immune scores from immune gene expression profiling or immune cell infiltration by mIF were associated with response to ICIs and improved survival, except regulatory T cells, which were associated with worse overall survival (OS) for patients receiving nivo+ipi. Immune cell density and closer proximity of CD8+GZB+ T cells to malignant cells were associated with superior progression-free survival and OS. The cold immune landscape of NSCLC was associated with a higher level of chromosomal copy-number variation (CNV) burden. Patients with LRP1B-mutant tumors had a shorter survival than patients with LRP1B-wild-type tumors. Olink assays revealed soluble proteins such as LAMP3 increased in responders while IL6 and CXCL13 increased in nonresponders. Upregulation of serum CXCL13, MMP12, CSF-1, and IL8 were associated with worse survival before radiologic progression.Conclusions:The frequency, distribution, and clustering of immune cells relative to malignant ones can impact ICI efficacy in patients with SqNSCLC. High CNV burden may contribute to the cold immune microenvironment. Soluble inflammation/immune-related proteins in the blood have the potential to monitor therapeutic benefit from ICI treatment in patients with SqNSCLC.

Supplementary Figure 8. Integration analysis identifies immune features associated with progression free survivial.

Supplementary Figure 3. Spatial organization and clustering in early progressors.

Supplementary Table 3. Frequently observed phenotypes in the two multiplex immunofluorescence panels.

To investigate the cellular and molecular mechanisms associated with targeting CD30-expressing Hodgkin lymphoma (HL) and immune checkpoint modulation induced by combination therapies of CTLA4 and PD1, we leveraged Phase 1/2 multicenter open-label trial NCT01896999 that enrolled patients with refractory or relapsed HL (R/R HL). Using peripheral blood, we assessed soluble proteins, cell composition, T-cell clonality, and tumor antigen-specific antibodies in 54 patients enrolled in the phase 1 component of the trial. NCT01896999 reported high (>75%) overall objective response rates with brentuximab vedotin (BV) in combination with ipilimumab (I) and/or nivolumab (N) in patients with R/R HL. We observed a durable increase in soluble PD1 and plasmacytoid dendritic cells as well as decreases in plasma CCL17, ANGPT2, MMP12, IL13, and CXCL13 in N-containing regimens (BV + N and BV + I + N) compared with BV + I (P < 0.05). Nonresponders and patients with short progression-free survival showed elevated CXCL9, CXCL13, CD5, CCL17, adenosine-deaminase, and MUC16 at baseline or after one treatment cycle and a higher prevalence of NY-ESO-1-specific autoantibodies (P < 0.05). The results suggest a circulating tumor-immune-derived signature of BV +/- I +/- N treatment resistance that may be useful for patient stratification in combination checkpoint therapy. Significance: Identification of multi-omic immune markers from peripheral blood may help elucidate resistance mechanisms to checkpoint inhibitor and antibody-drug conjugate combinations with potential implications for treatment decisions in relapsed HL.

Supplementary Table 2. Characteristics of multiplex immunofluorescence panels using Opal 7 IHC Kit (Akoya Biosciences).

Supplementary Table 1. Clinicopathologic from total patients and according to assay.

Supplementary Figure 7. LRP1B mutation associations with immune infiltration and survival.

Supplementary Figure 4. Neutrophil presence associated with exceptional response

Association of plasma cytokines with clinical benefit. A, C, and E, PFS survival analysis shows the Kaplan–Meier curves for VEGFR2, CXCL9, and MUC16, respectively. Statistics using log-rank test and Cox proportional hazard models are shown. Higher than median VEGFR2 levels were associated with slower progression whereas higher than median levels of CXCL9 and MUC16 were associated with faster progression. B, D, and F, Forest plots for VEGFR2, CXCL9, and MUC16, respectively. Here, we show the multivariate statistics for each of these proteins including sex, age, treatment type, and cancer stage (Ann Arbor stage). These figures verify the directionality of the KM curves shown and show that these three proteins are independent of these clinically relevant covariates. G, Shows the receiver operating characteristic (ROC) curves for the prediction of PFS using VEGFR2, MUC16, CXCL9, PDL1, and clinical variables. Ordered from most relevant to least relevant model, reflected on the AUC values. H, Univariate PFS Cox modeling of Olink analytes. I, Univariate OS Cox modeling of Olink analytes. Both I and H, show on the x-axis the HR and the y-axis shows the −log10 (P values) based on the log-rank test.

Supplementary Table 5. Overall associations between cell phenotypes by compartment in both arms.

Supplementary Table 7. Comparation of median densities between patients who experienced exceptional responses and early progression/death according to compartment.

Supplementary Table 6. Associations of genes with outcomes in both arms.