Combined MEK/PD-L1 inhibition alters peripheral cytokines and lymphocyte populations correlating with improved clinical outcomes in advanced biliary tract cancer

Abstract

Purpose

Biliary tract cancers (BTCs) are aggressive malignancies refractory to chemotherapy and immunotherapy. MEK inhibitor (MEKi)-based regimens may have utility in this disease when combined with PD-L1 blockade. We hypothesize that dual MEK/PD-L1 inhibition alters circulating soluble and cellular immune mediators to improve clinical outcomes in patients with advanced BTC.

Experimental Design

We examined immune features in peripheral blood from 77 patients with advanced BTC enrolled in a Phase II clinical trial investigating atezolizumab with or without cobimetinib. Plasma and PBMCs were isolated from whole blood to evaluate soluble factors and immune cell populations. Baseline blood samples were additionally compared with healthy donors to identify immune signatures unique to BTC.

Results

At baseline, the soluble factors PDGF-BB, PlGF-1, IL-5, and IL-17A were elevated in BTC patients compared to healthy adult donors, and higher baseline frequencies of CD8⁺BTLA⁺ T cells correlated with better overall survival in this trial. There were also significant treatment-related alterations in several factors, including decreased PDGF-BB following combination treatment, that correlated with improved OS and PFS. Higher baseline levels of IL-23 and RANTES corresponded to improved clinical outcomes following combination treatment. Dual MEKi/PD-L1 inhibition increased populations of CD4⁺TIM3⁺ and decreased CD8⁺VISTA⁺ T cells, correlating with worse OS and better PFS, respectively.

Conclusions

This work represents a comprehensive analysis of peripheral immune features in BTC patients and systemic responses to dual MEK/PD-L1 inhibition. These data support further investigation to understand how MEKi combines with immunotherapeutic approaches to improve clinical outcomes for advanced BTC patients.

Introduction

Biliary tract cancers (BTC) are a rare group of aggressive malignancies that include gallbladder cancer (GBC), intrahepatic cholangiocarcinoma (ICC), and extrahepatic cholangiocarcinoma (ECC). Biliary tract cancers collectively have a 5-year median survival rate of less than 10% and are typically diagnosed at advanced stage when curative surgical resection is not possible. These tumors are also refractory to most systemic treatment options. Precision medicine approaches in subsets of patients with tumors harboring FGFR2 fusions or IDH1/2 mutations offer promise, but other targeted therapies have not shown substantial benefit over chemotherapy(1–5). In considering pathways consistently altered in BTC, constitutive activation of Ras/Raf/MEK/ERK signaling is frequent in tumors from BTC patients(6). Activating KRAS mutations are present in approximately 22% of BTC cases, associating with poor prognosis and uncontrolled cell growth(7). This has prompted investigations with inhibitors of MAPK/ERK Kinase (MEK), which limit disease progression as single agents in BTC and have been tested in combination with immunotherapy in patients with other solid tumors, including melanoma and breast cancer(6–11).

Programmed cell death ligand 1 (PD-L1) is expressed on tumor cells heterogeneously among BTC patients, interacting with its receptor PD-1 to possibly limit cytotoxic T cell function and anti-tumor immunity(12). Immune checkpoint inhibitor (ICI) therapies including PD-L1 blockade have limited activity historically against BTC(13,14), but recent data from the phase III TOPAZ-1 trial show that adding durvalumab, a PD-L1-targeted antibody, to standard of care gemcitabine and cisplatin improves overall survival in the first-line for advanced BTC patients(15). As a result, immunotherapy has garnered major attention in the context of BTC, and further investigation of systemic immune features in these patients can inform future therapeutic strategies.

Previous pre-clinical studies demonstrate that MEK inhibition (MEKi) can enhance CD8⁺ T cell infiltration into tumors, while PD-L1 blockade invigorates CD8⁺ T cell mediated antitumor activity. Furthermore, MEKi synergizes with PD-L1 blockade to improve anti-tumor responses in several solid tumor models(16,17). Building off these studies, we recently published results from a clinical trial investigating atezolizumab (anti-PD-L1) with or without the MEK inhibitor cobimetinib in patients with advanced BTC (NCT03201458). This trial met its primary endpoint of improved progression-free survival (PFS), with a median PFS of 3.65 months in combination-treated patients, versus 1.87 months in patients receiving atezolizumab monotherapy(18).

In this study, we address the hypothesis that dual MEK/PD-L1 inhibition alters circulating soluble and cellular immune mediators to improve clinical outcomes in advanced BTC. Using a unique collection of peripheral blood samples from advanced BTC patients undergoing therapy with atezolizumab with or without cobimetinib, we provide a comprehensive analysis of immune features. Our data encompasses cytokines, chemokines, and phenotypically-defined immune cell subsets from a large cohort of patients, informing our understanding of how dual MEK/PD-L1 inhibition impacts immune markers during treatment. Finally, using baseline blood samples from patients and a cohort of healthy adult donors, we identify novel immune features prominent in BTC patients, suggesting potential avenues for future investigations of therapeutic targets.

Materials and Methods

Patients and Treatment

Peripheral blood was collected from 77 patients with metastatic, pathologically confirmed ICC, ECC, or GBC following informed consent (Table 1). Patients were enrolled in a randomized, national Phase II clinical trial (NCT03201458) of atezolizumab with or without cobimetinib(18). Patients randomized to Arm A received atezolizumab every two weeks, while patients randomized to Arm B received cobimetinib daily (21 days on/7 days off) alongside atezolizumab every two weeks. This work was carried out under a protocol approved by the National Cancer Institute’s (NCI) Cancer Therapy Evaluation Program (CTEP) and the central and local institutional review boards (IRBs). All patients were enrolled between February 2018 and October 2018. Whole blood samples were collected prior to treatment and on treatment at the start of cycle 2 (C2D1), then transported overnight to Winship Cancer Institute for processing and analysis. Due to the aggressive nature of BTC, we collected patient correlative samples at this time point to avoid missing samples from patients with disease progression or treatment intolerance. From a biologic standpoint, prior work suggests that early immune responses (within 4 weeks) are important indicators of favorable clinical outcomes to PD-1/PD-L1 targeted therapies (19). PBMCs and plasma were isolated from whole blood via density gradient centrifugation using Ficoll-Paque (GE Healthcare Bio-Sciences AB, Uppsala, Sweden). Additionally, we obtained samples from two cohorts of de-identified healthy donors, collecting 12 PBMC samples from buffy coats (Sylvan N. Goldman Oklahoma Blood Institute, Oklahoma City, OK) and 16 plasma samples from whole blood (Emory University Hospital, Atlanta, GA), used as comparators for our baseline data. Normal donor blood samples were processed similarly to patient samples by density gradient centrifugation. All patient and normal donor plasma were cryopreserved at −80°C until analysis, and all PBMCs were cryopreserved in liquid nitrogen prior to analysis. Blood samples received more than 2 days after collection were not used for PBMC isolation to ensure consistent quality but were still processed for plasma via centrifugation and used for bioplex analysis. As a result, some patients did not have viable samples at all timepoints, and hence data were collected using samples from 70 patients for PBMC analysis and 51 patients for plasma analysis. Patient sample exclusion criteria is detailed in Figure 1.

Table 1:

NCT03201458 Patient Demographics

Patient Characteristics	N=77
Age
>=44, <65	43
>=65, <86	34
Sex
Female	48
Male	29
Tumor Type
Extrahepatic cholangiocarcinoma (ECC)	14
Intrahepatic cholangiocarcinoma (ICC)	43
Gallbladder carcinoma (GBC)	20
Prior Therapies
1	47
2	30

Figure 1. Patient sample exclusion diagram.

Number of samples excluded and reasons for exclusion at each step of analysis. Created with BioRender.com.

Cytokine, chemokine, and growth factor analysis

Plasma samples were analyzed using a panel of 45 cytokines, chemokines, and growth factors on a Luminex magnetic bead-based platform according to manufacturer protocol (ProcartaPlex Cytokine/Chemokine/Growth Factor Panel 1, EPX450–12171-901, ProcartaPlex Immunoassays, Invitrogen; Waltham, MA). Samples were analyzed in duplicate and batch-run on a Luminex 100 instrument and quantified using analyte-specific standard curves for each batch. Only 51 patients who remained on treatment through C2D1 blood collection were analyzed. Quality filtering based on a coefficient of variation of >15% was performed for each analyte. Fifteen analytes were excluded from analyses for >50% of measurements falling below the detectable assay threshold across patient samples.

Flow cytometry

Comprehensive phenotypic analysis of peripheral immune cells was conducted via 23-color flow cytometry. Antibodies are detailed in Supplementary Table S1. Cryopreserved PBMCs were thawed at 37°C, washed, centrifuged, and resuspended in FACS buffer (PBS + 3% FBS + 0.05 mM EDTA). Cells were incubated with surface antibody for 30 min at 4°C, washed, permeabilized and fixed overnight using the FoxP3/Transcription Factor Staining Buffer set (00–5523-00, eBioscience). Cells were incubated with intracellular antibodies for one hour at room temperature, washed, and resuspended in FACS buffer for analysis. Flow cytometric analysis was conducted on a Cytek Aurora (Cytek Biosciences, Fremont, CA). Compensation controls were generated using UltraComp eBeads Compensation Beads (01–2222-41, Invitrogen; Waltham, MA). Data were analyzed using FlowJo software version 10.7.2 (FlowJo, LLC; Ashland, OR).

Statistical analyses

Descriptive statistics were used to summarize patients’ demographics and disease characteristics. Biomarker values at baseline and their changes to C2D1 were first summarized and associated with treatment group, then further linked with clinical outcomes (e.g., OS, PFS). To account for data dependency rooted in repeated samples from the same patients, two-way repeated measures ANOVA test, along with Šídák’s multiple comparisons test, was used to test the interaction effect between percent change and treatment groups. This approach permits determination of whether combination treatment leads to more change in biomarker measurements than single agent treatment (Arm B vs Arm A). All biomarker measurements were compared between patient and healthy donor samples using two-sided Student’s t-test. For either baseline biomarker levels or their changes to C2D1, univariate (UVA) and multivariate (MVA) associations with clinical outcomes (OS, PFS) in each treatment arm were explored using the Cox proportional hazards model, illustrated using the Kaplan-Meier method, and significance was determined by log-rank test. The measures of all biomarkers were dichotomized on the entire sample (< median vs. >= median) for univariate and multivariate analyses. Soluble factors were dichotomized by percent change, and immune populations were dichotomized by fold change. Multivariate analyses were adjusted simultaneously for age, sex, and anatomic tumor subtype as relevant clinical variables. To correct for multiple testing, the Benjamini and Hochberg method was used to control the false discovery rate (FDR) on both UVA and MVA, with a cut-off value of p<0.25 for significance. Significance was adjusted separately for soluble factors and for immune cell populations. Due to limited statistical power from a small sample size, significance for multivariate analyses was determined as p<0.1, while significance of all other analyses was determined as p<0.05.

Data Availability

The data generated in this study are available within the article and its supplementary data files or otherwise available upon request from the corresponding author.

Results

Differential production of cytokines, chemokines, and growth factors in the blood of BTC patients compared to healthy donors

To define prominent immune features in patients with BTC, independent of treatment, we compared biomarker data at baseline from this patient cohort with that of healthy donors. We analyzed a panel of 45 soluble factors in peripheral plasma samples, which evaluated cytokines regulating T cell differentiation and function, inflammatory cytokines, chemokines, and growth factors. Fifteen markers, including a subset of pro-inflammatory cytokines, were below the detectable assay threshold in >50% of all plasma samples and concentrations could not be accurately determined. Therefore, these fifteen factors were excluded from further analyses. Of the remaining 30 soluble factors, 21 were significantly elevated among BTC patients (Figure 2A, Supplementary Table S2). In particular, PDGF-BB, IL-5, IL-17A, and PlGF-1 were all elevated five-fold more than what was observed in healthy donors (p≤0.002) (Figure 2B–E). The remaining 9 factors were not present at concentrations significantly different from healthy donors. Independent of treatment effect from the clinical trial, there were no correlations between any soluble factors at baseline with clinical outcomes (data not shown).

Figure 2. Biliary tract cancer patients have distinct soluble factor signatures compared to healthy donors.

(A) Heat map of cytokines, chemokines, and growth factors among BTC anatomic subtypes that are differentially expressed from healthy donors. Soluble factor measurements are in pg/mL. Several factors are significantly elevated in BTC patients compared to healthy donors across all disease subtypes, including (B) PDGF-BB, (C) PlGF-1, (D) IL-5, and (E) IL-17a. Comparisons between BTC patients and healthy donors were evaluated by Student’s t tests. ***P<0.001 ****P<0.0001.

PD-1 and BTLA-expressing T cells are elevated in BTC patients

Multiparameter 23-color flow cytometric analysis of PBMCs was used to interrogate unique immune landscapes in blood of advanced BTC patients (Figure S1). Our analysis encompassed 19 phenotypically distinct populations including lymphocytes, myeloid cells, and cells expressing immune checkpoint proteins. We also used this flow panel to conduct analysis of a separate cohort of 12 healthy adult PBMCs and found that 9 phenotypically-defined cell populations were significantly different between BTC patients and healthy donors (Supplementary Table S3). Circulating total CD8⁺ T cells were lower overall in BTC patient samples (p=0.003), but of those CD8⁺ T cells, BTC patients had higher frequencies of cells expressing inhibitory checkpoint ligands. Notably, CD8⁺PD-1⁺ (p=0.007) and CD8⁺BTLA⁺ (p=0.016) cells were present at higher frequencies in BTC patients. Additionally, CD4⁺PD-1⁺ (p=0.001) and CD4⁺BTLA⁺ (p=0.001) T cells were higher in BTC patients, as well as PD-1⁺TIM3^- activated CD8⁺ T cells (p=0.004) (Figure 3A–E, Supplementary Table S3). In contrast, CD4⁺ and CD8⁺ T cells expressing TIM3, LAG3, and VISTA were not present in BTC patients at frequencies different from healthy donors. We also assessed relationships between distinct immune phenotypes identified and clinical parameters in this cohort of metastatic BTC patients. We postulated peripheral biomarkers may emerge to predict overall survival and signify more aggressive disease. Interestingly, the frequency of BTLA⁺CD8⁺ T cells correlated with OS, where patients with above-median population frequencies had longer OS, regardless of treatment received on the clinical trial (p=0.036) (Figures 3E, F, Supplementary Table S3).

Figure 3: T cells with an activated phenotype are significantly elevated in BTC patients, and BTLA⁺CD8⁺ T cells elevated above median at baseline correlate with better overall survival.

Baseline populations of (A) CD4⁺PD-1⁺, (B) CD4⁺BTLA⁺, (C) CD8⁺PD-1⁺, and (D) CD8⁺PD-1⁺TIM3^- T cells are elevated in BTC patients compared to healthy donors. (E) BTLA⁺CD8⁺ T cells are significantly elevated in BTC patients compared to healthy donors. (F) Kaplan-Meier curve of BTC patients stratified by median population of CD8⁺BTLA⁺ T cells depicting that above-median populations at baseline are associated with better overall survival. Comparisons between BTC patients and healthy donors were evaluated by Student’s t tests. Association with overall survival was explored by the Cox proportional hazard model and significance determined by log-rank test. **P<0.01, ***P<0.001.

MEK inhibition significantly alters growth factor levels when combined with anti-PD-L1 therapy in advanced BTC that correlate with improved clinical outcomes

To assess the effect of both single-agent treatment with atezolizumab and dual therapy with cobimetinib, we calculated percent change of plasma concentrations of soluble factors from baseline to C2D1 (Figure 4A). We stratified all patients by median percent change for each soluble factor, then evaluated correlations between percent change and clinical outcomes within each treatment group (Supplementary Table S4). For patients in Arm B, dual treatment with atezolizumab and cobimetinib significantly decreased plasma concentrations of PDGF-BB (p=0.0456), BDNF (p=0.0036), and PlGF-1 (p<0.0001) from baseline to C2D1, while patients in Arm A receiving atezolizumab monotherapy had no significant changes in plasma concentration for these analytes (Figure 4B–D). Of these factors, the decrease in plasma concentrations of PDGF-BB in Arm B was associated with improved OS (UVA p=0.023, FDR=0.345; MVA p=0.084, FDR=0.921) and PFS (UVA p=0.040, FDR=0.360; MVA p=0.067, FDR=0.395) on exploratory univariate analysis and in multivariable analysis. Given the limited sample size however, these trends were no longer statistically significant upon false discovery rate (FDR) adjustment for multiple comparisons (Figure 4E, Supplementary Table S4). Changes in concentration of other soluble factors correlated with improved clinical outcomes for patients in Arm A and Arm B, but could not be directly associated with treatment effects following two-way ANOVA (Supplementary Table S4). We next evaluated baseline plasma concentrations of soluble factors as predictors of response to cobimetinib and atezolizumab combination therapy (Supplementary Table S5). Baseline levels were stratified by median concentration of each analyte as a whole, then evaluated for correlations with clinical outcomes within each treatment group. Higher baseline concentrations of IL-23 in Arm B patients correlated with improved overall survival (UVA p<0.001, FDR=0.03; MVA p=0.02, FDR=0.60), while higher baseline concentrations of BDNF (UVA p=0.063, FDR=0.330; MVA p=0.017, FDR=0.170), IL-8 (UVA p=0.005, FDR=0.075; MVA p=0.013, FDR=0.170) and RANTES (UVA p<0.001, FDR=0.030; MVA p=0.001, FDR=0.030) indicated better PFS (Supplementary Table S5), both in univariate exploratory analysis and after adjusting for multiple comparisons. Baseline plasma concentrations of other soluble factors in both Arm A and Arm B had relationships to clinical outcomes that trended toward significance by UVA and/or MVA (Supplementary Table S5). Measurement of these factors at baseline may be predictive of how advanced BTC patients respond to dual blockade of MEK and PD-L1.

Figure 4. Dual MEK/PD-L1 inhibition alters soluble factor levels that correlate with clinical outcomes.

(A) Heat map depicting mean percent change in soluble factor plasma concentrations from baseline to C2D1 across all patients within each treatment arm (Arm A n=28, Arm B n=23). Changes in soluble factor concentrations from baseline (open circles) to C2D1 (closed circles) for (B) PDGF-BB, (C) PlGF-1, and (D) BDNF. Soluble factor measurements are in pg/mL. (E) Kaplan-Meier plot of PFS for Arm B patients stratified by median percent change in PDGF-BB concentration from baseline to C2D1, where patients with a decrease in PDGF-BB had improved PFS. Comparisons between treatment arms and changes in soluble factor concentrations were evaluated by two-way repeated measures ANOVA. Association with PFS was explored using the Cox proportional hazard model and significance was determined by log-rank test. *P<0.05, **P<0.01, ****P<0.0001.

Regulation of T lymphocyte populations with an exhausted phenotype by dual MEK/PD-L1 blockade correlates with improved clinical outcomes

From baseline to C2D1, combination therapy was associated with an increase in CD4⁺TIM3⁺ T cells, as evidenced by a notable interaction via two-way ANOVA (p_int=0.032, Figure 5B). However, this increase correlated with worse overall survival for these patients (UVA p=0.015, FDR=0.285; MVA p=0.016, FDR=0.219); Figure 5C; Supplementary Table S6). Both overall survival and progression-free survival were improved for single-agent treated patients with increasing populations of CD4⁺BTLA⁺ (UVA_OS p=0.006, FDR=0.057, MVA_OS p=0.013, FDR=0.247; UVA_PFS p=0.003, FDR=0.029, MVA_PFS p=0.002, FDR=0.038) and CD8⁺BTLA⁺ T cells (UVA_OS p=0.005, FDR=0.057, MVA_OS p=0.060, FDR=0.418; UVA_PFS p=0.003, FDR=0.029, MVA_PFS p=0.02, FDR=0.019). Additionally, Arm A patients with an increase in CD4⁺LAG3⁺ T cells from baseline to C2D1 also had improved OS (UVA p=0.014, FDR=0.089; MVA p=0.066, FDR=0.418), while decreasing CD8⁺VISTA⁺ T cells correlated with improved PFS for Arm B patients (UVA p=0.001, FDR=0.019; MVA p=0.003, FDR=0.057). Changes in other immune populations from baseline to C2D1 had correlations with clinical outcomes trending toward significance (Supplementary Table S6).

Figure 5. Inhibition of MEK and PD-L1 promotes increased populations of TIM3-expressing CD4 lymphocytes but leads to worse overall survival.

(A) Gating schema for identifying CD4⁺TIM3⁺ T cells. (B) Changes in populations of CD4⁺TIM3⁺ T cells as percent of CD4⁺ T cells from baseline (open circles) to C2D1 (closed circles). (C) Kaplan-Meier plot of OS for Arm B patients stratified by median fold change in CD4⁺TIM3⁺ T cell populations from baseline to C2D1, where increased populations correlate with worse OS. Comparisons between treatment arms and changes in immune cell populations were evaluated by two-way repeated measures ANOVA. Association with OS was explored using the Cox proportional hazard model and significance was determined by log-rank test. *P<0.05.

High baseline CD8⁺ T cells correlate with improved overall survival following dual MEK/PD-L1 blockade

The predictive value of baseline populations of peripheral immune cells on clinical outcomes was next evaluated (Supplementary Table S7). Baseline populations of immune cells were stratified by median percent of parent population. In Arm B patients, higher peripheral CD8⁺ T cells (UVA p=0.016, FDR=0.304; MVA p=0.138, FDR=0.836) and CD8⁺ cells that were BTLA⁺ (UVA p=0.061, FDR=0.5795; MVA p=0.092, FDR=0.836) correlated with improved OS. Patients in this arm with fewer peripheral CD8⁺VISTA⁺ T cells also had better OS (UVA p=0.036, FDR=0.228; MVA p=0.125, FDR=0.497) and improved PFS (UVA p=0.018, FDR=0.342; MVA p=0.09, FDR=0.656). Above-median populations of intermediate monocytes in single-agent treated Arm A patients also correlated with better OS (UVA p=0.027, FDR=0.228; MVA p=0.013, FDR=0.247). These findings were significant upon exploratory univariate and multivariate analyses, but due to small sample size were no longer significant when adjusted for multiple comparisons.

Discussion

This study provides a comprehensive profile of the systemic immune landscape of advanced BTC and evaluates how MEK inhibition and anti-PD-L1 therapy modulate systemic immune factors and their relationship to clinical outcomes. We assess peripheral immune biomarkers in pretreatment samples to profile the immune landscapes of a large cohort of advanced BTC patients. We identified several soluble factors, including PDGF-BB, IL-5, IL-17A, and PlGF-1, that are elevated in plasma of BTC patients when compared to healthy donors, as well as CD4⁺ and CD8⁺ T cells expressing PD-1 and BTLA. In our clinical trial, dual MEK/PD-L1 blockade altered production of soluble factors in patient plasma compared to PD-L1 blockade alone, including a decrease in PDGF-BB that correlated with improved PFS. Further, changes in several T cell populations expressing immune checkpoint markers were observed in patients receiving combined MEK/PD-L1 inhibitors and correlated with improved clinical outcomes.

The use of MEK inhibition (MEKi) to enhance PD-1/PD-L1-targeted immunotherapy has been investigated in various preclinical tumor models and in clinical trials. While we observed correlations between important immune factors and survival outcomes, our overall clinical experience with dual MEK/PD-L1 inhibition in advanced BTC patients revealed a modest extension of PFS compared to PD-L1 blockade alone. The low objective response rate in both treatment arms in this study reflect the immune resistant nature of BTC and the need to understand mechanisms of response and resistance to immunotherapy in BTC patients. Although MEKi has been shown to promote CD8⁺ T cell infiltration and limit TCR-mediated exhaustion, previous work from our group shows systemic MEK administration can also inhibit T cell activation and function. In particular, the addition of MEKi to PD-L1 blockade in this trial limited expansion of T cell populations with activated phenotypes, whereas anti-PD-L1 alone increased these cells(20). Fortunately, subsequent preclinical studies showed that this could be overcome through co-treatment with antibodies that act as agonists to T cell function, such as 4–1BB(20). Inspired by these results, we have initiated a follow-up randomized trial investigating the combination of cobimetinib and atezolizumab with varlilumab, an agonistic mAb for CD27, to restore T cell activation (NCT04941287). MEKi and PD-L1 blockade have distinct functions in controlling the immune response to solid tumors, which may explain why specific correlations to OS and PFS were evident that were not related to overall response rates. We hope our work interrogating other immune features associated with clinical benefit will guide development of future treatment options for these patients(18).

Cytokine signatures have been surveyed to predict responders to immunotherapy in other tumor models and may have utility in advanced BTC patients(21). Several factors, including PlGF-1 and PDGF-BB, that are markedly higher in plasma from BTC patients compared to healthy individuals, also have an established relationship with the biology of BTC tumors(22–24). For example, placental growth factor (PlGF-1) is present at high levels in blood from ICC patients and associated with desmoplasia and disease progression(23). Collectively this factor promotes aggressive disease, and its inhibition limits progression in hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA) tumor models(23,25). Like PlGF-1, platelet derived growth factor B (PDGF-BB) is prominently produced by myofibroblasts in hepatobiliary tumors and promotes pro-survival signaling in BTC(26). Plasma levels of PDGF-BB were substantially reduced in BTC patients receiving cobimetinib with atezolizumab versus atezolizumab alone, and this decrease correlated with improved PFS in combination treated patients. PDGF-BB signals exclusively through the PDGFRβ receptor, activating downstream MEK/ERK and PI3K/AKT signaling(24,27,28). PDGFRβ is associated with epithelial-to-mesenchymal transition (EMT) to promote invasion and metastasis in colorectal cancer(29). To further support a relationship between MEK signaling and PDGF-BB, this growth factor has a critical mitogenic role in fibroblasts and other stromal cells in the tumor microenvironment, promoting stromal cell activation and angiogenesis(30,31). Since peripheral PDGF-BB was also elevated at baseline in BTC patients versus healthy donors, and PDGFRβ is expressed in human CCA tumors, targeting of the PDGF-BB/PDGFRβ axis likely has a role in controlling BTC. Previous studies have demonstrated genetic and pharmacologic inhibition of PDGFRβ promotes apoptosis and reduces tumor growth in in vivo models of BTC(26). However, further investigation is necessary to elucidate mechanistic relationships between PDGF-BB and BTC for potential use in future therapeutic strategies.

Analysis of peripheral immune populations showed notable changes in both CD4⁺ and CD8⁺ T cells expressing immune checkpoint proteins associating with clinical outcomes in patients receiving combination therapy. Atezolizumab alone led to higher LAG3⁺ and BTLA⁺ T cell populations correlating with better OS and PFS, while atezolizumab and cobimetinib combination therapy led to fewer CD8⁺VISTA⁺ T cells that correlated with improved PFS. Conversely, combination therapy significantly increased the frequency of CD4⁺TIM3⁺ T cells, correlating with worse PFS. These data were consistent with prior studies in melanoma whereby MEKi also increased TIM3 expression on lymphocytes, and higher TIM3 expression frequently correlates with poor clinical outcomes in several other cancer models(32,33). Further investigations following this clinical trial may benefit from addition of agents targeting TIM3, which could be more effective in combination with other checkpoint inhibitors(33). In addition to treatment-related changes in T cell populations, analysis of peripheral immune cells at baseline revealed higher frequency of CD8⁺BTLA⁺ T cells versus healthy donors, which correlated with increased overall survival in this BTC patient cohort. These results prompt further questions related to the role of BTLA on peripheral T cells as a reflection of BTC progression. BTLA is a co-stimulatory molecule in the CD28 immunoglobulin superfamily that harbors classical inhibitory signaling motifs(34,35). However, the role of BTLA as a regulator of T cell mediated immune responses to cancer is not straightforward. Regulation of immune checkpoint expression and function is key to controlling the immune response in many cancers, but there has been little investigation into associations between MEKi and BTLA, LAG3, or VISTA expression. Indeed, there are documented molecular connections between activation of ERK with downstream transcription factors that control expression of genes encoding checkpoint proteins, though further investigation is required to fully characterize the relationship between MEKi and immune checkpoint expression on immune cells.

While this study lends meaningful insight into systemic immune features in BTC patients, it does have limitations. Since the trial was open at multiple sites and patients had unresectable metastatic disease, the feasibility of obtaining tissue to probe intratumoral immune features was limited, with a greater sample size of peripheral blood specimens. Our data suggest peripheral blood is useful for understanding systemic immune alterations that accompany advanced cancer and may reveal provocative differences for further study. Although our patient population was substantial for biliary tract cancer studies, sample sizes in our analyses were much smaller than other clinical trials. In particular, data in our analyses of treatment effects was dichotomized by the median value of each analyte before stratification by treatment arm for correlation with clinical outcomes. Furthermore, to make our conclusions more succinct, we describe changes in immune factors from baseline to C2D1 as either increases or decreases depending on which side of the median they fall on. This simplifies some quantitative details where the median is not precisely at no fold or percent change, but makes our conclusions more concise overall. We also acknowledge this study was only conducted in the United States, and therefore does not encapsulate the pathological manifestations of BTC common in other regions such as liver fluke infection. Additionally, our comparison of baseline samples to healthy donors does not account for inflammatory diseases of the liver and biliary tract, including hepatitis, primary sclerosing cholangitis, and gallbladder disease, that could influence the peripheral immune factors in our patient population. Finally, targeted therapy against FGFR2 fusions and IDH alterations are among the most promising areas of clinical development for BTC, but we did not have extensive genome-level data on tumors from patients enrolled in this trial to consider for advancing this line of study(36,37). Despite these understandable limitations, our comprehensive analysis of immune cell and soluble factor signatures in the blood of advanced BTC patients is of value to the field.

Overall, this study investigates how MEKi combines with anti-PD-L1 to modulate clinically-relevant, systemic soluble factors and immune populations in a large cohort of patients with advanced metastatic BTC. We have also delineated differences in peripheral immune markers of BTC patients compared to healthy individuals. This work advances our understanding of how MEKi synergizes with immune checkpoint blockade and has uncovered factors that merit additional study for a disease with few effective treatment options.

Statement of Translational Relevance.

Biliary tract cancers (BTCs) are a rare but aggressive group of gastrointestinal malignancies that are highly heterogeneous and have dismal clinical outcomes. Patients are often diagnosed at advanced stages with unresectable metastatic disease, and effective treatment options are extremely limited. This work details a comprehensive immune profiling of advanced BTC patients enrolled in a clinical trial investigating anti-PD-L1 blockade by atezolizumab with or without the MEK inhibitor cobimetinib. We report alterations in soluble factors and immune cell populations in peripheral blood following combination therapy that correlate with improved clinical outcomes. We also identify baseline immune factors uniquely elevated in BTC patients that can inform better clinical screening for BTC and serve as new targetable factors to advance the development of effective therapies for this disease.