Cluster of differentiation 40 agonism as a priming strategy in pancreatic ductal adenocarcinoma: biomarker insights from OPTIMIZE-1

Overcoming the immunosuppressive tumor microenvironment (TME) of pancreatic ductal adenocarcinoma (PDAC) remains a significant unmet need. To date, clinical success with immune-checkpoint inhibition in PDAC has remained elusive and despite the intensification of chemotherapy, minimal improvements in survival over the past several decades have been made (1-5). Multiple factors contribute to this treatment resistance including a dense fibrotic extracellular matrix (ECM), poor vascularity, and the dominance of immunosuppressive cell populations (6-8). This milieu is characterized by tumor-associated macrophages (TAMs), tumor-associated neutrophils (TANs), myeloid derived suppressor cells (MDSCs) and the paucity of anti-tumor immune cells such as CD8 T cells, dendritic cells (DCs) and natural killer cells (NKs) (9-11). Collectively, these hallmark features of PDAC result in T cell exclusion, thus strategies that induce stromal degradation and allow for both T cell activation and infiltration are a major focus of ongoing investigation.

Agonism of cluster of differentiation 40 (CD40), a member of the tumor necrosis factor receptor (TNFR) superfamily, represents a promising therapeutic approach for inducing a pro-inflammatory phenotype. CD40 is primarily expressed by antigen presenting cells (APCs), macrophages, and B lymphocytes and the binding of CD40 ligand (also known as CD154), expressed on activated T cells, licenses APCs to increase major histocompatibility complex (MHC) surface expression, enhances pro-inflammatory cytokine release, and ultimately primes naïve CD4⁺ and CD8⁺ T cells (12-18). In pre-clinical PDAC models, CD40 agonists polarize tumor TAMs to an M1 (pro-inflammatory/anti-tumor) phenotype which infiltrate tumors and facilitate degradation of tumor stroma, resulting in tumor collapse and improved chemotherapy delivery (19,20). Together, these combined effects act to remodel the TME and provide the mechanistic basis for therapeutic investigation. Early clinical experience combining agonist CD40 monoclonal antibodies with chemotherapy demonstrated increased T cell infiltration, reduced tumor fibrosis and produced tumor regressions in patients with PDAC, thus prompting the optimization of sequential and combinatorial approaches (21-23). In this context, the phase 1b/2 OPTIMIZE-1 clinical trial was designed to evaluate the CD40 agonist, mitazalimab, in combination with modified FOLFIRINOX (mFOLFIRINOX; 5-fluorouracil, leucovorin, irinotecan, oxaliplatin) for patients with treatment naïve, metastatic PDAC (24,25).

In the full analysis set of OPTIMIZE-1, 57 efficacy-evaluable patients received the CD40 agonist, mitazalimab, at the recommended phase-2 dose (900 µg/kg) as a priming dose on Cycle 1 Day 1, followed by biweekly cycles of mFOLFIRINOX plus mitazalimab. The confirmed objective response rate (ORR) reached 42.1%, with 24 partial responses (PRs) and one complete response (CR)—a rate that is similar to slightly improved over the response rate previously reported in the phase 3 mFOLFIRINOX (31.6%) and NALIRIFOX (liposomal irinotecan, 5-fluorouracil, leucovorin, and oxaliplatin) (42%) studies (5,26). The disease control rate (DCR) was 78.9%, with a median progression-free survival (PFS) rate of 7.7 months. Notably, the median overall survival (OS) was 14.9 months which is encouraging when compared with historical controls for first line standard of care chemotherapy alone (27,28). Notably, nearly one-third of patients (n=18/57) remained on treatment beyond 12 months, suggesting the possibility of durable benefit in a substantial subset of patients. Importantly, the safety profile was consistent with that of mFOLFIRINOX alone, with the most common grade ≥3 treatment-emergent adverse events being primarily hematologic (neutropenia, anemia, thrombocytopenia), resulting in discontinuation of treatment in only 7% of patients (n=5/57) (5,27).

Within the landscape of CD40 agonism for the treatment of advanced/metastatic PDAC, three agents have been studied in clinical trials: mitazalimab, selicrelumab and sotigalimab (Table 1). Several features of the OPTIMIZE-1 study design are particularly distinctive. In contrast to other trials which administered CD40 agonists (selicrelumab and sotigalimab) concurrently with chemotherapy, OPTIMIZE-1 introduced a priming dose of mitazalimab seven days prior to mFOLFIRINOX, with the intention of inducing CD40 activation of immune cells and stromal degradation, thus preconditioning of the TME for subsequent chemotherapy. To leverage the increased antigen presentation induced by chemotherapy-induced tumor cell killing, following the priming dose, mitazalimab was given two days after each cycle of mFOLFIRINOX to further maximize CD40 mediated T cell activation. While the results of OPTIMIZE-1 represent the most compelling data for CD40 agonism in metastatic PDAC, cross-trial comparison is difficult due to the impact from differences in chemotherapy backbone. In the selicrelumab trial conducted by Beatty et al., a gemcitabine monotherapy backbone was utilized whereas in the phase 2 PRINCE trial, sotigalimab was given with combination gemcitabine/nab-paclitaxel with or without nivolumab (21,23). Thus, drawing cross-trial conclusions regarding response rates and survival are undoubtedly confounded by the known differences in chemotherapy regimen (gemcitabine monotherapy vs. gemcitabine/nab-paclitaxel vs. mFOLFIRINOX) rather than a reflection of CD40 agonist activity. Furthermore, mechanistic, differences in Fc gamma receptor (FcγR) engagement amongst CD40 agonists may also partially explain variance in outcomes. Unlike antagonistic antibodies [e.g., anti-programmed death-1 (anti-PD-1)/programmed death ligand-1 (PD-L1)], CD40 agonists require cross-linking to enable downstream immune activation (29). Mitazalimab and sotigalimab (both part of the IgG1 subclass) are dependent on FcγR-engagement; however, selicrelumab (IgG2 subclass) is not dependent on the Fc domain, but rather relies on its IgG2 hinge configuration to cross-link CD40 (30-33). Thus selicrelumab, as an FcγR-independent antibody, is thought to mediate a less controlled immune activation, which has historically limited dose escalation due to cytokine release syndrome (23). Amongst the two IgG1 CD40 agonists, mitazalimab and sotigalimab differ in their Fc modifications with mitazalimab having a wild-type Fc domain whereas sotigalimab has a mutation in its Fc region (S267E) resulting in enhanced affinity for FcγRIIB and FcγRIIA, and decreased affinity for FcγRIIIA (30). As FcγRIIB expression is enriched on B cells, sotigalimab is expected to preferentially increase CD40 agonist activity in B cell rich compartments such as in the blood and secondary lymphoid organs (34,35). In contrast, mitazalimab, with its wild-type Fc region, enables engagement with a wider range of FcγRs including FcγRI and FcγRIII which are expressed on macrophages, DCs and NK cells, thus resulting in potentially lower activity in the blood, but higher in the TME (34-37). These distinctions underscore that CD40 agonists are not interchangeable and suggest that agent-specific differences in FcγR engagement may influence both efficacy and safety profiles.

Taken together, the OPTIMIZE-1 results offer important insights into CD40 agonist therapeutics, suggesting that clinical impact may be dependent not only by CD40 agonist agent selection and partner chemotherapy, but also by dosing strategy and treatment sequencing.

OPTIMIZE-1 integrates longitudinal multi-omic and immunophenotypic analysis aimed at identifying biological correlates of response. In long term survivors (>12 months), RNA sequencing (RNAseq) identified an increase in a baseline fibrosis-related gene signature, suggesting this may predict a population with favorable outcomes with this approach. This profile demonstrated enrichment of genes involved in immune-modulatory pathways (S100A4 and IL6), collagen fibril organization and ECM remodeling (MMP2, MMP9, and COL15A1) which fits with the proposed mechanism of action of CD40 agonism (19,38,39). In contrast, short survivors (<12 months) demonstrated increased expression of chemotherapy resistance pathways including increased drug metabolism (CYP450 enzymes and UGT inactivating pathways) and drug efflux pump pathways which would be less impacted by TME modulatory changes expected with CD40 agonism (40). However, in the absence of a comparator arm, it remains unclear whether these sensitivity and resistance signatures are influenced by mitazalimab treatment or simply reflect a tumor subtype more intrinsically susceptible to chemotherapy. While not directly answering this question, stratification by baseline molecular subtype (classical vs. basal-like) demonstrated that patients with a baseline molecular classical subtype experienced longer OS compared to those with basal-like tumors which is a known predictor to FOLFIRINOX-based therapy (41,42). KRAS mutation status also appeared to influence treatment response and survival, with the G12V variant more prevalent among treatment responders (CR/PR) compared with non-responders (stable disease/progressive disease) whereas patients harboring the G12D variant exhibited a trend toward shorter OS, a trend consistent throughout the literature with other treatment approaches (43,44). This important biomarker work highlights the biological heterogeneity of PDAC and will help guide patient selection for a biomarker-driven PDAC study or patient stratification in a randomized trial.

A longstanding challenge in chemoimmunotherapy trials has been distinguishing the relative contributions of chemotherapy and investigational therapies, particularly in single-arm studies. The design of OPTIMIZE-1, with a priming dose of mitazalimab prior to chemotherapy, allowed for the assessment of peripheral immune cell populations after mitazalimab alone rather than being confounded by effects from chemotherapy. Cell populations collected from peripheral blood at Cycle 1 Day 2 and Cycle 1 Day 8 (i.e., prior to chemotherapy) revealed increased numbers of Ki67⁺ CD8⁺ T effector memory cells, Ki67⁺ CD4⁺ T central memory cells and Ki67⁺ natural killer T cells, which were associated with longer OS. Paired tumor biopsies, while limited to three patients with a PR, demonstrated via differential gene expression analysis (DGEA), upregulation of both myeloid (CSF2RB, TLR2, S100A8, FLT3LG, CCR1, and FCGRIIIB) and T cell (CD83, CD274, and CCL4) responses. To explore this observation, recapitulation using a PDAC tumor-bearing mouse model was performed which demonstrated an increase in intratumoral Ki67⁺ effector CD8⁺ T cells, monocytes and macrophages following treatment with mitazalimab. Taken together, these results provide additional evidence of CD40 agonism demonstrating intratumoral myeloid and cytotoxic lymphocyte activation. One limitation which the authors acknowledge however is the variance in biopsy timing relative to clinical response (between 6 and 86 days) and deconvolution of the TME using bulk RNA-seq, which precluded spatial evaluation of TME architecture. In the design of future studies, incorporation of on-treatment biopsies, including responders and non-responders, will be necessary to mechanistically validate the anticipated TME dynamics in response to this immunotherapeutic approach. For instance, the formation of intratumoral tertiary lymphoid structures has been associated with improved responses to immunotherapy in contemporary PDAC immunotherapy trials (45,46) underscoring the importance of incorporating prospective spatial proteomic and transcriptomic profiling which could provide insights into immune cell localization, functional states, and cell-cell interactions in response to therapy.

Looking to the future, with the rapidly evolving landscape of KRAS-directed therapies, future trials will most certainly need to adapt to these treatment paradigms. Recent pre-clinical data has demonstrated that both KRAS G12D mutation specific and pan-RAS inhibitors remodel the TME in a manner favorable for combinatorial immunotherapy approaches, through increasing MHC I expression, decreasing MDSCs, increasing CD8⁺ T cell infiltration, and changing the polarity of TAMs to an immune-permissive M1 phenotype (47-51). In one study, when given to immunocompetent mice with PDAC, RMC-6236 (pan-RAS inhibitor), led to PDAC regressions, with decreasing MDSCs and CD103⁺ DCs and increasing T cells and macrophages in the TME (51). When administered with an immunotherapy regimen consisting of CD40 agonist, anti-CTLA-4 and anti-PD-1, RMC-7977 (a preclinical tool compound representative of RMC-6236) significantly augmented the depth of tumor regressions by 7 days and led to 10/10 regressions (100%) at 25 days compared to 4/7 regressions (57%) with RMC-7977 as single agent (51). These pre-clinical data suggest that KRAS inhibition may synergize with immune priming strategies such as CD40 agonism, providing mechanistic basis for further evaluation.

In summary, OPTIMIZE-1 provides compelling clinical and biological evidence that mitazalimab, when administered as a priming agent prior to chemotherapy may enhance the efficacy of mFOLFIRINOX in metastatic PDAC without compromising safety. OPTIMIZE-1 reinforces several emerging themes in PDAC therapeutics. First, it underscores the feasibility in reprogramming the TME through immune conditioning and highlights the importance of sequential strategies. Second, it emphasizes the utility of patient stratification based off heterogenous baseline characteristics and through multi-omic analyses, this study further reinforces the integration of molecular subtype (classical vs. basal-like), fibrosis-related gene signatures, and KRAS allele status may enable more precise patient selection status in future trials. While a confirmatory, randomized, phase 3 comparison is warranted, these findings support the feasibility of incorporating immune conditioning with CD40 agonism as a viable strategy to overcome the immunosuppressive PDAC TME.

Acknowledgments

None.

Footnote

Provenance and Peer Review: This article was commissioned by the editorial office, Annals of Pancreatic Cancer. The article has undergone external peer review.

Peer Review File: Available at https://apc.amegroups.com/article/view/10.21037/apc-26-0010/prf

Funding: None.

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://apc.amegroups.com/article/view/10.21037/apc-26-0010/coif). K.L.L. received grants from the American Society of Clinical Oncology (ASCO) and the James and Frances McGlothlin Foundation. E.S.C. received grants from Affimed GMBH, Parabilis, Haystack, Incyte, NextCure, Pfizer, Regeneron. E.S.C. receives consulting fees from Boston Scientific, Parabilis, Roche, Seres Therapeutics, SirTex, Tatum Biosciences, and Urogen. E.S.C. received support for travel from NextCure. The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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