Galon, Jérôme

Multiplex immunofluorescence and immunohistochemistry (mIF/IHC) are increasingly employed antibody-based technologies that use tissue sparingly and facilitate the detection of co-localized or neighboring biomarkers. Specifically, these platforms enable spatial analyses of the tumor microenvironment as well as extended applications, for example, describing normal tissue anatomy, autoimmunity, infectious diseases, etc. mIF/IHC has greatly enhanced biomarker discovery efforts, and a growing number of studies suggest superiority to traditional IHC. Standardization of staining approaches, reporting of image analysis strategies and resultant data is critical for facilitating cross-study comparisons, validation, deployment, and generalization of findings. To address this challenge, The Society for Immunotherapy of Cancer (SITC) previously published two articles providing best practice guidelines for mIF/IHC staining, image analysis, and data sharing. Here, SITC convened stakeholders to develop the third article in the series, a consensus checklist for scientific reporting of mIF/IHC data to support and complement the best practice guidelines. The checklist includes critical components of mIF/IHC applications to be defined within publications such as detailed descriptions of analytical validation; image acquisition, selection, and registration methods; and cell clustering and spatial analysis strategies, amongst others. Such information will help with data reproducibility and comparison across studies towards future drug and assay development.

Background: Axi-cel is a US and EU-approved autologous anti-CD19 chimeric antigen receptor (CAR) T cell therapy for pts with relapsed/refractory large B cell lymphoma after 1 prior therapy as a result of ZUMA-7 (NCT. In ZUMA-1 (NCT02348216), the pivotal study which initially led to the approval of axi-cel in the 3L+ setting, the objective response rate was 83% (58% complete response rate; Locke et al. Lancet Oncol. 2019) and at 5 years follow up an OS rate of 42.6%, 5 years durable response and 5 years disease specific survival at 51% ( Neelapu et al. Blood 2023). T cell-related biology (Immunosign 21; Immunoscore®IC) measured pretreatment in the tumor microenvironment (TME) was associated with response to axi-cel ( Scholler et al., 2022). Increased density of activated PD-1+LAG-3+/−TIM-3−CD8+ T cells, measurable pretreatment, facilitates clinical response in pts post-axi-cel. This expanded analysis characterized the impact of axi-cel treatment on the TME immune contexture and examined associations between immune cell subsets and relapse. Methods: Baseline and post-infusion tumor biopsy samples were analyzed by multiplex immunohistochemistry ( Brightplex®). Four panels were developed and applied to assess T cell infiltration (CD3 CD8 FOXP3 TIM3 PD1 LAG3 TOX), regulatory T cell subtyping (CD3 CD8 GATA3 TBET RORg BCL6), T cell activation & exhaustion (CD3 CD8 TIM3 LAG3 PD1 GZMB KI67), macrophage (CD68 CD64 CD163 CD204 CD206 PDL1) and MDSC (CD3 CD11B CD68 CD14 CD15 LOX1 S100A9) subsets. Transcriptomic analysis was performed using nCounter® Pan Cancer panel. The association between T cell subtypes and macrophages cell subset density, and probability to relapse was evaluated. T test values were based on Brightplex analysis. Results: 34 tumor biopsies (16 at baseline, 18 post-infusion) from 26 pts were analyzed including 11 relapsed (6 CR/5 PR) and 15 durable response (15 CR). The baseline and post-infusion TME comprised all major macrophages, MDSC and T cell subsets, with diverse distribution across samples analyzed. Low proinflammatory M1 macrophage density was observed at baseline and post-infusion across patients. In relapsed patients, baseline tumors were enriched with a significantly higher proportion of protumoral M2 macrophage (p<0.0001). A shift of M1-M2 polarization was observed between baseline and post-infusion (Baseline 33%M1M2 / 65%M2, post infusion 76%M1M2 / 21%M2, p<0,0001) across the cohort. In contrast, in relapsed patients, M-MDSC cell density was significantly increased post treatment (p=0.0420). In addition, Innate lymphocyte cells subtype 2 (ILC2) cell density, previously described to be positively correlated with improved overall survival in CRC ( Huang et al. 2021 Cancers), was significantly decreased in relapsed patients post infusion (p=0.019).Post-infusion, durable response was associated with a significant increase in pan-Macrophages (p=0.0360) and cytotoxic T cell subset densities: CD8 naïve T cells (p=0.016); Activated Cytotoxic T cell (GZMB+)(p=0.0203); PD1+TIM3+LAG3- cytotoxic T cell (p=0.0117); CD8 regulatory T cell (FOXP3+)(p=0.012). Regarding T helper lineage, ongoing response was associated with a significant increase of: CD4 naïve T cells (p=0.021); T helper Th2 (p=0.020); cytotoxic T lymphocyte TC2 and TC1/TC2 cell densities (respectively p=0.0052 and 0.011).Additional characterization of the immune contexture and correlative analysis of cell subsets will be presented. Conclusion: These results suggest that baseline proportions of protumor M2 macrophages may predict disease relapse after axi-cel treatment. Following infusion, relapse is also associated with spatial enrichment of M-MDSC, whereas durable response is related to increases in T cell subpopulation densities inclusive of cytotoxic T cells, T helper, TC1/TC2 ratios and innate T-subpopulations. These findings suggest that axi-cel treatment drastically impacts tumor immune mobilization, infiltration and contexture, which correlates with long term response.

Abstract (a) Expansion of therapeutic strategies against diffuse large B-cell lymphoma (DLBCL) offers new opportunities to fight aggressive non-Hodgkin's lymphoma. Therapy approaches, such as R-CHOP and stem cell transplant, and CAR-T cell therapies are now available for the treatment of DLBCL. While the role of the tumor microenvironment (TME) during the therapeutic response is now recognized, the spatial organization and the communication between the diverse factors that compose the TME remains to be fully understood. In this study, we describe a new immunogram tool to help clinical researchers measure the tumor microenvironment contribution to drug efficacy or toxicity in DLBCL context. (b) In a previous work, using a high-quality testing and innovative multimodal and integrative approach, we constructed the first Veracyte Biopharma Atlas for DLBCL lesions. Based on this knowledge and the understanding of the immune contexture of tumors, we constructed an immunogram using the scoring of clinically relevant biomarkers. Our immunogram scoring approach is composed of key features driving computational derivation of DLBCL Atlas clusters, which include immune gene signatures, immune cell phenotyping combined with spatial information, and genomic and clinical parameters. (c) Patterns that strongly associated with CAR-T treatment response were determined. In addition, promising results have been obtained in the prediction to CAR-T toxicity related to T-regulatory cells or R-CHOP response with transcriptomic signature. (d) Retrospective analysis of CAR-T clinical trial data and tumor biopsies highlighted the Veracyte Immunogram as a powerful tool to score the patient probability of response to CAR-T. This work provides the framework for identification of predictors of response to therapy, potential targets of interest or biomarkers that could be applied in future clinical trials. Citation Format: Michael D. Mattie, Regis Perbost, Sarah Turcan, Corinne Danan, Frederick L. Locke, Sattva S. Neelapu, David B. Miklos, Caron A. Jacobson, Lazaros J. Lekakis, Yi Lin, Armin Ghobadi, Jenny J. Kim, Zixing Wang, Allen Xue, Simone Filosto, Nathalie Scholler, Jerome Galon. Defining the cancer Immunogram using multimodal analysis to guide immunotherapy in diffuse large B cell lymphoma [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 5879.

PURPOSE The recommended duration of adjuvant fluoropyrimidine and oxaliplatin chemotherapy for patients with stage III colon cancer is based on tumor classification into clinically low-risk (T1-3 N1) and high-risk (T4 or N2) groups. We determined whether Immunoscore can enhance prognostication within these risk groups. MATERIALS AND METHODS Patients with stage III colon carcinomas (N = 600) were randomly selected from the infusional fluorouracil, leucovorin, and oxaliplatin arm of adjuvant trial NCCTG N0147 (Alliance for Clinical Trials in Oncology). Tumors were evaluated for Immunoscore that quantifies CD3+ and CD8+ T-cell densities in the tumor center and invasive margin by digital image analysis. Disease-free survival (DFS) by Immunoscore was analyzed using a multivariable Cox regression model in each risk group with adjustment for covariates including KRAS, BRAF V600E, and mismatch repair status. RESULTS Of 559 cancers with Immunoscore data, 299 (53.5%) were classified as clinically low-risk (T1-3 N1) and 260 (46.5%) as clinically high-risk (T4 and/or N2). Among patients with low-risk tumors, those with Immunoscore-Low versus Immunoscore-High tumors had significantly worse 5-year DFS rates (77.5% v 91.8%; hazard ratio, 1.70; 95% CI, 1.03 to 2.79; P = .037). Among patients with high-risk tumors, those with Immunoscore-Low versus Immunoscore-High tumors also had significantly worse DFS (55.3% v 70.3%; hazard ratio, 1.65; 95% CI, 1.11 to 2.47; P = .013). Tumors that were low-risk/Immunoscore-Low had similar outcomes as did tumors that were high-risk/Immunoscore-High ( P = .174). Prognostication was significantly improved in multivariable models where Immunoscore was added to clinical risk parameters and limited biomarkers (likelihood ratio test P = .0003). CONCLUSION Immunoscore can refine patient prognosis beyond clinical risk group classification, suggesting its potential utility for adjuvant decision making.

Abstract Background: Conventional prognostic factors for LBCL were not associated with outcomes in the pivotal ZUMA-1 study of axi-cel in relapsed LBCL (Neelapu et al. NEJM. 2017); however, other attributes like chimeric antigen receptor (CAR) T-cell fitness and composition (CCR7+CD45RA+ T cells), reduced preTx tumor burden, and immune tumor microenvironment (TME) with presence of activated CD8+PD-1+LAG-3+/-TIM-3- T cells were associated with efficacy (Locke et al. Blood Adv. 2020; Galon et al. ASCO 2020. #3022). Here, we evaluated preTx immune cell phenotypes in premanufacturing apheresis (premfg aph) material, comprising peripheral blood mononuclear cells, to determine associations with product attributes, immune TME features, and clinical efficacy in ZUMA-1. Methods: Evaluable samples from patients (pts) in Phase (Ph) 1 and Ph2 Cohorts (C) 1-3 were analyzed (NCT02348216; Ph1 and Ph2 C1+2, ≥2-y follow-up; C3, ≥6-mo follow-up). Memory T, myeloid, NK, NKT, and B cells in premfg aph material (n=101, excluding C3) were characterized by flow cytometry (FC). PreTx immune TME was analyzed by multiplex IHC (n=18) and gene expression analysis (n=30) as previously described (Rossi et al. AACR 2018. #LB-016; Galon et al. ASCO 2020. #3022). CAR T-cell fitness was analyzed by doubling time, viability during manufacturing, and product T-cell phenotypes by FC (n=145). Associations between these covariates, and with routine hematology tests, were performed by Spearman rank correlation or Wilcoxon tests. Effects on survival were assessed by Kaplan-Meier with optimized cutpoint selection. Results: The percentage (%) of naive (CCR7+CD45RA+) T helper (Th; CD4+CD127+CD25low) cells coexpressing CD27 and CD28 (median, 1%; range, 0.01%-15.8%; IQR, 0.3%-3.8%) in aph associated positively with axi-cel efficacy. The % of intermediate monocytes (IMs; CD14+CD16+; median, 1.8%; range, 0.003%-16.7%; IQR, 1%-3%) in aph associated negatively with efficacy. The % of circulating CD27+CD28+ naive Th cells associated positively with an enriched preTx immune TME T-cell signature, % CCR7+CD45RA+ product T cells, objective response rate, PFS, and OS. An increased % of IMs associated directly with negative predictive markers (preTx serum levels of LDH, IL-6, and CRP) and inversely with TME T-cell signature, PFS, and OS. The premfg ratio of CD27+CD28+ naive Th cells/IMs associated directly with CAR T-cell expansion and efficacy. Conclusions: This work points to a link between the pre-existing state of the immune system, reflected in premfg aph, and immune TME, as well as product attributes influencing axi-cel efficacy in LBCL. These data bear practical implications towards the development of predictive biomarkers for axi-cel efficacy. [JB and JC contributed equally.] Citation Format: Justin Budka, Justin Chou, Vicki Plaks, Francesca Milletti, Zixing Wang, Frederick L. Locke, Sattva S. Neelapu, David B. Miklos, Caron A. Jacobson, Lazaros J. Lekakis, Yi Lin, Armin Ghobadi, Zahid Bashir, Nathalie Scholler, Jérôme Galon, John M. Rossi, Adrian Bot. Pretreatment (PreTx) immune cell phenotypes in peripheral blood associated with the tumor immune contexture, product attributes, and durable clinical efficacy in patients with large B-cell lymphoma (LBCL) treated with axicabtagene ciloleucel (axi-cel) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr CT166.

3022 Background: Axi-cel is a US and EU-approved autologous anti-CD19 chimeric antigen receptor (CAR) T cell therapy for pts with relapsed/refractory large B cell lymphoma after ≥ 2 prior therapies. In ZUMA-1 (NCT02348216), the objective response rate was 83% (58% complete response rate; Locke et al. Lancet Oncol. 2019). T cell-related biology (Immunosign 21; Immunoscore) measured pretreatment in the tumor microenvironment (TME) was associated with response to axi-cel (Rossi et al. AACR 2018. #LB-016; Rossi et al. AACR 2019. #CT153). This expanded analysis characterized the pretreatment TME immune contexture and examined associations between immune cell subsets and response. Methods: In ZUMA-1, pts received axi-cel at a target dose of 2.0 × 106 CAR T cells/kg. Archival pretreatment tumor biopsy samples were analyzed by multiplex immunohistochemistry (Brightplex). Two panels were developed and applied to assess T cell (CD3, CD8, FoxP3, PD-1, LAG-3, TIM-3) and myeloid cell (CD11b, CD14, CD15, LOX1, S100A9, CD68) subsets (n = 14 total). The association between T cell and myeloid cell subset density, prespecified immune scores (Immunosign 21; Immunoscore), and objective response was evaluated. T test values were based on Brightplex analysis. Results: Pretreatment tumor biopsy samples from 18 pts were analyzed (14 objective responders and 4 nonresponders). The pretreatment TME comprised all major myeloid and T cell subsets, with diverse distribution across samples analyzed. The median TME density of monocytes (CD11b+ CD15− CD14+; 1215 cells/mm2) and macrophages (CD68+; 530 cells/mm2) was greater than that of the total CD8+ T cell subset (312 cells/mm2). The pretreatment Immunosign 21 and Immunoscore scores associated positively with the density of all major T cell subsets and some myeloid subsets. The density of activated CD8+ T cells (PD-1+ LAG-3+/− TIM-3−) was most significantly associated with clinical response versus other T cell subsets. The density of nonactivated CD8+ T cells (PD-1− LAG-3− TIM-3−) and exhausted CD8+ T cells (PD-1+ LAG-3+ TIM-3+) were not significantly associated with response. Additional characterization of the immune contexture and correlative analysis of cell subsets will be presented. Conclusions: These results suggest that a TME associated with increased density of activated PD-1+ LAG-3+/− TIM-3− CD8+ T cells, measurable pretreatment, facilitates clinical response in pts post–axi-cel.

Abstract Background The American Joint Committee on Cancer staging and other prognostic tools fail to account for stage-independent variability in outcome. We developed a prognostic classifier adding Immunoscore to clinicopathological and molecular features in patients with stage III colon cancer. Methods Patient (n = 559) data from the FOLFOX arm of adjuvant trial NCCTG N0147 were used to construct Cox models for predicting disease-free survival (DFS). Variables included age, sex, T stage, positive lymph nodes (+LNs), N stage, performance status, histologic grade, sidedness, KRAS/BRAF, mismatch repair, and Immunoscore (CD3+, CD8+ T-cell densities). After determining optimal functional form (continuous or categorical) and within Cox models, backward selection was performed to analyze all variables as candidate predictors. All statistical tests were two-sided. Results Poorer DFS was found for tumors that were T4 vs T3 (hazard ratio [HR] = 1.76, 95% confidence interval [CI] = 1.19 to 2.60; P = .004), right- vs left-sided (HR = 1.52, 95% CI = 1.14 to 2.04; P = .005), BRAF V600E (HR = 1.74, 95% CI = 1.26 to 2.40; P < .001), mutant KRAS (HR = 1.66, 95% CI = 1.08 to 2.55; P = .02), and low vs high Immunoscore (HR = 1.69, 95% CI = 1.22 to 2.33; P = .001) (all P < .02). Increasing numbers of +LNs and lower continuous Immunoscore were associated with poorer DFS that achieved significance (both Ps< .0001). After number of +LNs, T stage, and BRAF/KRAS, Immunoscore was the most informative predictor of DFS shown multivariately. Among T1–3 N1 tumors, Immunoscore was the only variable associated with DFS that achieved statistical significance. A nomogram was generated to determine the likelihood of being recurrence-free at 3 years. Conclusions The Immunoscore can enhance the accuracy of survival prediction among patients with stage III colon cancer.

Abstract Background: Axi-cel is an FDA-approved autologous anti-CD19 chimeric antigen receptor (CAR) T cell therapy for pts with relapsed/refractory large B cell lymphoma after ≥ 2 prior therapies. In Phase 2 of ZUMA-1, the objective response rate was 83% (58% complete responses [CRs]) (Locke et al. Lancet Oncol. 2018). Previous work showed that pre-existing expression of immune-related genes (Immunosign® 21 with NanoString®) may be associated with response to axi-cel (Rossi et al. AACR 2018. #LB-016). This expanded post-hoc analysis includes novel analysis associating T cell density with efficacy. Methods: Pts received a target dose of 2.0 × 106 CAR T cells/kg in ZUMA-1 (NCT02348216). Pretreatment (before conditioning) tumor tissues were analyzed by immunohistochemistry for density of T cell subsets (CD3+, CD8+). A prespecified bioinformatics algorithm was used to generate a numerical index (Immunoscore®; Galon et al. J Pathol. 2014) and analyses cutoffs. Higher Immunoscore® represents increased intra-tumor T cell infiltration. TME was evaluated for Immunosign® 21 and for additional myeloid- and dendritic cell-related genes. The association between Immunoscore®, CD3+, or CD8+ T cell density and CR was evaluated, as was the association of Immunoscore® with Immunosign® 21. Results: Pretreatment tumor biopsy samples from 25 pts were analyzed (data cutoff Aug 11, 2017). Of pts with high Immunoscore®, 77% (7/9) had CR and 11% (1/9) were nonresponders. Of those with low Immunoscore®, 38% (6/16) were nonresponders and 50% (8/16) had CR. The pts in this category who achieved CR had a lower median pretreatment tumor burden by sum of product diameters. Overall, higher pretreatment Immunoscore® was associated with achievement of CR (P = .048). Pretreatment intra-tumor densities of CD3+ and CD8+ T cells separately quantified, were also positively associated with achievement of CR (P = .02 and .036 by t test). In addition, higher pretreatment Immunosign® 21 was associated with clinical efficacy. Values for Immunosign® 21 and Immunoscore® demonstrated 82% concordance (95% CI, 65-93; r2 = 0.451). Finally, TME gene expression analysis indicated a pan-inflammatory profile, including myeloid- and dendritic cell-related gene expression, in pts who achieved CR with higher Immunosign® 21 and Immunoscore®. Conclusions: These results link pre-existing T cell involved lymphoma TME with clinical outcome in patients post axi-cel. They also suggest that factors intrinsic to tumor biology may influence CAR T cell efficacy through the immune microenvironment. In addition, this treatment modality may overcome an unfavorable TME in a subset of patients with low disease burden. Finally, these results support CAR T cell treatment optimizations designed to overcome an immune detrimental TME. John Rossi and Jérôme Galon contributed equally. Citation Format: John M. Rossi, Jérôme Galon, Edmund Chang, Regis Perbost, Nathalie Scholler, Sarah Turcan, Corinne Danan, Frederick L. Locke, Sattva S. Neelapu, David B. Miklos, Caron A. Jacobson, Lazaros J. Lekakis, Yi Lin, Armin Ghobadi, Francesca Milletti, William Y. Go, Adrian Bot. Pretreatment immunoscore and an inflamed tumor microenvironment (TME) are associated with efficacy in patients (Pts) with refractory large B cell lymphoma treated with axicabtagene ciloleucel (Axi-Cel) in ZUMA-1 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr CT153.