Combined targeted and epigenetic-based therapy enhances antitumor immunity by stabilizing GATA6-dependent MHCI expression in pancreatic ductal adenocarcinoma

Domain: neuroscience
Published: 2026-02-06
Editorial outcome: major_revision

Abstract
GATA6 promotes epithelial phenotypes and limits epithelial-to-mesenchymal (EMT) transition in pancreatic ductal adenocarcinoma (PDAC). Here we show that GATA6 defines a tumor cell state that induces MHCI expression and anti-tumor cytotoxicity upon therapy. In human PDAC, GATA6 expression correlates with immune cell infiltration, and spatial analysis reveals interaction between GATA6+ tumor cells and CD8+ T cells. In murine PDAC, MEK inhibition (MEKi) enriches antigenicity-related gene sets in GATA6 high cells, while GATA6 knockout or degradation impairs MEKi-induced MHCI upregulation. High-GATA6 tumors respond to MEKi with increased MHCI, enhancing T-cell cytotoxicity, whereas GATA6 loss abolishes this effect. Treatment-induced EMT reduces GATA6+ populations and MHCI expression, which is restored by combining MEKi with HDAC inhibitors, enhancing GATA6+ tumor cells, MHCI, CD8+ T cell infiltration, tumor suppression, and survival. These findings suggest that therapeutic strategies promoting a GATA6-driven tumor cell state improve immune recognition of PDAC cells and potentiate anti-tumor cytotoxic effects.

Introduction
GATA6 is a key regulator of the classical PDAC phenotype, maintaining epithelial differentiation while restraining epithelial-to-mesenchymal transition (EMT)1. Low GATA6 expression is linked to the basal-like subtype and poorer survival outcomes1–5. Retrospective studies have shown strong associations between high GATA6 expression and prolonged survival in treatment-naive and advanced PDAC cases2,6,7. However, these survival associations are inconsistent following neoadjuvant therapy, likely due to phenotypic plasticity, cell state transitions, and the emergence of intermediary tumor phenotypes during treatment1,8–11. Beside subtype classification, GATA6 influences immune surveillance. Its loss in PDAC is associated with impaired antigen processing, reduced CD8+ T-cell infiltration12, and increased immunosuppressive myeloid cells in basal-like tumors13. Given its pivotal role in shaping tumor phenotype and immune interactions, it is crucial to dissect the dynamic role of GATA6 and its interactions with the TME during therapeutic responses in PDAC.

Frequent downregulation of major histocompatibility complex (MHC) I is a hallmark of solid tumors and is implicated in conferring resistance to immunotherapy14,15. Targeted inhibition of MEK (MEKi), a key kinase within the mitogen-activated protein kinase (MAPK) pathway, has emerged as a strategy to modulate tumor immunogenicity and reshape the immune microenvironment16–20. However, despite the strong preclinical rationale, the clinical response of PDAC to MAPK pathway inhibition has been disappointing21,22. Notably, treatment-induced phenotypic transitions associated with EMT have been observed, underscoring the adaptive nature of tumor subtypes and highlighting the imperative to disrupt regulatory pathways governing cellular plasticity23,24, supporting that tumor subtypes are adaptive and denote the importance of perturbing regulatory pathways underlying cellular plasticity. Histone deacetylase inhibitors (HDACi) are known to promote epithelial gene expression in tumor cells and reduce pro-tumorigenic stromal activation in PDAC25,26, offering a strategy to combat tumor heterogeneity and overcome therapy resistance.

HDACi, including Suberoylanilide Hydroxamic Acid (SAHA), mocetinostat, and domatinostat, have been demonstrated to enhance the expression of antigen-presenting machinery genes and MHC molecules, alongside promoting infiltration of cytotoxic T lymphocytes (CTLs), across various solid cancers27,28.

Here, we investigated GATA6-dependent immunological responses to MEKi using preclinical PDAC models with pronounced intratumor heterogeneity. Through multiplex spatial analysis, we identified MEKi-induced MHCI upregulation that was specific to GATA6+ tumor cells, which, however, was reduced in proportion due to parallel MEKi-induced mesenchymal cell state switch. By targeted GATA6 degradation, we found a regulatory function for GATA6 in MEKi-induced MHCI expression. Notably, when combined with HDACi, the MEKi-responsive GATA6+ tumor population was preserved, leading to further enhancement of MHCI expression and anti-tumor cytotoxicity. This study investigates the cellular interaction between tumor cells undergoing cell state switches with cells of the immune compartments upon therapeutic perturbation, unveiling a GATA6-dependent anti-tumor cytotoxic response. Therapeutic strategies that aim at restoring and augmenting the abundance of GATA6+ tumor cell states may enhance immune recognition of PDAC cells and potentiate anti-tumor cytotoxic effects.

Results
GATA6 expression associates with immunoreactive stroma in human PDAC
We assessed GATA6 expression specifically in tumor epithelial cells in a tissue microarray (TMA) from PDAC resections (n=143) by IHC. Epithelial regions were manually annotated for each of the 594 cores by a trained pathologist before GATA6 expression was digitally quantified. All cores per patient were averaged, and patients were categorized into GATA6 high, GATA6 intermediate, or GATA6 low groups by expression quantiles (Fig. S1a, Supplementary Dataset 1). The abundance of a panel of immune markers that was previously assessed in the cohort29 was then compared as a function of GATA6 expression in tumor cells. We found significantly higher levels of CD4+, CD20+, FOXP3+, CD68+, and tumor-infiltrating CD3+ and CD8+ cells in the high GATA6 expression group (Fig. 1a, Supplementary Dataset 1). Besides, a positive correlation was observed between tumor GATA6 and E-cadherin expression (Fig. S1a, Supplementary Dataset 1), supporting the specific expression of GATA6 in the classical PDAC subtype.

To dissect the role of GATA6 in tumor cells and their associated stroma, we analyzed the RNA-sequencing dataset of Maurer et al.30 (GSE93326, n=65), where PDAC malignant epithelium and stroma were procured by Laser Capture Microdissection (LCM). Patients were dichotomized into high (n=32) and low (n=32) tumor GATA6 expression groups (cutoff: median). Gene set enrichment analysis (GSEA) was performed and revealed strong immunological features in the high GATA6 epithelium group and their paired stroma (Fig. 1b, Supplementary Dataset 2). Notably, gene sets including inflammatory response, allograft rejection, TNFα via NFκB, IL2-STAT5, cytokine cytokine receptor interaction signaling are all commonly enriched in both GATA6 high epithelium and the paired stroma. In addition, IL6-STAT3, leukocyte migration signaling are enriched in GATA6 high epithelium, and leukocyte transendothelial migration, antigen processing and presentation, interferon gamma response, and immune response to tumor cell signaling are enriched in GATA6 high stroma (Fig. 1b, Supplementary Dataset 2).

Next, we investigated the cellular interaction of GATA6+ tumor cells. We performed CellChat analysis with a public PDAC single-cell RNA-sequencing (scRNA-seq) dataset (GSE212966, n=6), to predict cellular interaction between GATA6+/− tumor cells and different cell types and found that GATA6+ tumor cells demonstrated stronger interaction specifically with CD8+ T cells, CD68+ macrophages and CD20+ B cells than GATA6− counterparts (Fig. 1c). GSEA from a comparison between GATA6+ and GATA6− tumor cells showed that GATA6+ tumor cells were enriched for genes involved in inflammatory response pathways including TNFα via NFκB, cytokine activity, chemokine receptor binding and response to chemokines (Fig. 1c, Supplementary Dataset 3), which was consistent with the GSEA results in GSE93326 (Fig. 1b, Supplementary Dataset 2).

To validate the cellular interaction of GATA6+ tumor cells with immune cells at spatial level, we performed highly multiplex spatial imaging with Phenocycler on 8 therapy-naive human PDAC samples (Figs. 1d and S1b, c). Among these, 2 were resected PDAC primary tissues, while the remaining 6 cases were tissue microarray (TMA) cores of resected PDAC primary tumors (Fig. S1b, c, Supplementary Table 1). Four regions with heterogeneous GATA6 tumor expression were selected for imaging and analysis (Fig. S1b) from resected PDAC tissues, while whole tissue cores were included for analyses for TMA tissues (Fig. S1c). Average values of different ROIs or tissue cores were used for subsequent statistical analyses. 27 markers were included in the panel (Supplementary Dataset 6), and among which, 10 markers were analyzed for this study, focusing on the status of GATA6+ tumor cells and their interaction with immune cells (Fig. 1d). Proximity analysis was performed to measure the number of various immune populations in close proximity (<50 μM radical distance) of GATA6+ PanCK+ cells and GATA6− PanCK+ cells, respectively (Fig. 1d). The analyses revealed a significantly closer cellular interaction of GATA6+ PanCK+ cells with CD8+ cells, compared to GATA6− tumor cells. Similar trends can be observed for CD4+, CD68+, and CD20+ cells (Fig. 1d), although without statistical significance, given low sample numbers.

Since antigen presentation-related gene sets were enriched in both tumor and stroma compartment of high GATA6 tumors (Fig. 1b), we examined and compared the expression of MHCI (HLA-A) in GATA6+ PanCK+ and GATA6− PanCK+ cells, and found that GATA6+ tumor cells express higher levels of MHCI than their negative counterpart (Fig. 1e), implying the higher antigenicity of GATA6+ tumor cells. To better elucidate the status of MHCI+ GATA6+ tumor cells, we examined the proportion of proliferative marker PCNA+ cells within the MHCI+ PanCK+ population, as well as in MHCI+ GATA6+ PanCK+ cells. We observed no significant difference, suggesting that MHCI and GATA6 expression levels may not be associated with the proliferation status of tumor cells (Fig. S1d).

GATA6 is associated with the classical PDAC subtype1–5. We examined its co-expression with claudin 18 (CLDN18), which was recently linked to the classical and immunogenic PDAC subtype5,31. Additionally, keratin 5 (KRT5), a basal-like subtype marker32, was included for subtype analysis (Fig. S1e). Analysis of eight PDAC samples revealed a high degree of tumor subtype heterogeneity based on these four markers. Notably, GATA6 and CLDN18 did not co-express with KRT5 in any of the cases, supporting their classification as classical subtype markers. As expected, KRT5 expression was restricted to a small proportion of tumor cells, leaving a substantial fraction of the tumor cell population unclassified. Moreover, not all CLDN18+ classical tumor cells expressed GATA6, and vice versa (Fig. S1e), highlighting intrinsic heterogeneity within the classical subtype. Interestingly, GATA6+ CLDN18− PanCK+ cells, but not GATA6- CLDN18+ PanCK+ cells, were in close proximity with CD8+ T cells (Fig. S1f), suggesting a potential immunogenic role of the GATA6+ tumor subpopulation within classical tumor cells. Therefore, we hypothesized that this GATA6+ tumor subpopulation might be a target population for tumor-immune interactions and therapeutic perturbation.

MEK inhibition induces GATA6+ tumor cell-specific MHCI upregulation
We next investigated the impact of targeted MEK inhibition on GATA6 tumor cell expression and the immunological responses of GATA6+ tumor cells. Six primary cell lines (511950, 60400, 70301, 60531, 511892, 60590) derived from spontaneous PDAC of six different Ptf1awt/Cre;Kraswt/LSL-G12D;Trp53loxP/loxP (CKP) mice and one primary cell line (110299) from spontaneous PDAC of Ptf1awt/Cre;Kraswt/LSL-G12D;Trp53loxP/R172H (KPC) mouse33 were established. In addition, we included two Kraswt/LSL-G12D;Trp53 wt/LSL-R172H;Pdx1-Cre;Rosa26YFP/YFP (KPCY) derived cell lines 2838c3 and 6694C2, which were tumor cell clones generating tumors that recapitulated T cell-inflamed and non-T cell-inflamed TME in vivo, respectively34. We measured their GATA6 expression at protein level by flow cytometry (Fig. 2a) and dichotomized the cell lines into GATA6 high (110299, 511950, 2838c3, 60400, 70301) and GATA6 low (60531, 511892, 6694C2, 60590) groups (cutoff: median). We next generated corresponding long-term MEKi-treated counterparts from six of them (three GATA6 high: 511950, 60400, 70301 vs three GATA6 low: 60531, 511892, 60590) upon long-term trametinib treatment as previously described35 and compared their transcriptomic profiles (Fig. 2b). Notably, we observed enrichment of several immune-related gene sets, including allograft rejection, antigen processing and presentation, IFNα and IFNγ responses upon MEKi treatment only in GATA6 high, but not in GATA6 low cell lines (Fig. 2b, Supplementary Dataset 4).

Impaired antigen presentation machinery and MHCI downregulation are frequently observed in PDAC and are considered as pivotal mechanisms for tumor immune evasion during the development of drug resistance. We assessed the surface MHCI expression upon MEKi treatment in four GATA6 high and four GATA6 low murine cell lines by flow cytometry. Intriguingly, significant induction of surface MHCI was observed upon MEKi treatment in GATA6 high, but not in GATA6 low cell lines (Fig. 2c). Notably, cell proliferation assays confirmed that there was no significant change in cell proliferation upon treatment (Fig. S2a), suggesting that the increase in MHCI in GATA6 high cell lines was not associated with changes in cell proliferation.

Targeted GATA6 degradation ablated MEK inhibition-induced MHCI upregulation
To confirm the role of GATA6 in MEKi-induced MHCI expression, we generated two GATA6 knockout cell lines from the GATA6 high cell line 2838c3 (Figs. 2d and S2b). Results show that MEKi-induced MHCI expression was completely abolished in GATA6 knockout cell lines (Fig. 2e), suggesting that MEKi upregulates surface MHCI expression of tumor cells in a GATA6-dependent manner.

Since cells may develop compensatory mechanisms in response to GATA6 loss, to specifically investigate the role of GATA6 in MEKi-induced MHCI expression in PDAC cells, we generated an auxin-inducible degron (AID) knock-in cell line for targeted GATA6 protein degradation (AID-GATA6) and corresponding wild-type (WT) (Figs. 2f and S2c–f). The murine primary PDAC cell line 11029933 was selected due to its high endogenous GATA6 expression (Fig. 2a). GATA6 degradation was observed after 8 h of incubation with 5-Ph-IAA, with more pronounced degradation after 24 h treatment (Figs. 2g and S2g). Both 110299 WT and 110299 AID-GATA6 cells were treated with 5-Ph-IAA for 24 h, followed by MEKi treatment for an additional 48 h. Upon MEKi treatment, significant MHCI upregulation was detected in 110299 WT cells and 110299 AID-GATA6 cells, which were treated with 5-Ph-IAA, but not 110299 AID-GATA6 cells treated with 5-Ph-IAA (Fig. 2h), further supporting the role of GATA6 in MEKi-induced MHCI regulation.

GATA6 is essential for MEKi-induced tumor control and anti-tumor cytotoxicity in vivo
To investigate the role of GATA6 in MEKi-induced effects in vivo, we established syngeneic orthotopic tumor models using one GATA6 high (110299) and one GATA6 low (60590) cell line and treated them with MEKi (Fig. 3a). First, we examined GATA6 expression in the tumors and confirmed that the orthotopic tumors reflected the distinct GATA6 expression levels of the cell lines (Fig. 3b). Upon MEKi treatment, we observed no significant effect on the growth of GATA6 low tumors (60590), while it significantly suppressed the growth of GATA6 high tumors (110299) (Figs. 3c and S3a, b).

Histological analyses of GATA6 high tumors revealed elevated MHCI expression following MEKi treatment (Fig. 3d). Additionally, we observed increased infiltration of CD8+ and granzyme B (GzmB)+ cells, with cytotoxicity reflected by higher levels of cleaved caspase-3 (Cl casp3) in MEKi-treated tumors (Fig. 3d). Our previous analysis showed that MEKi-treated GATA6 high tumor cells were enriched for IFNγ response signature (Fig. 2b). Since IFNγ is a key regulator for MHCI expression on tumor cells, we deduced that GATA6 degradation might disrupt IFNγ response that led to ablated MHCI expression on the tumor cells. Therefore, we assessed pSTAT1, a key downstream signaling molecule of IFNγ pathway responsible for regulating MHCI expression36. The results showed strong pSTAT1 expression in GATA6 high tumors, with even more intense pSTAT1 signals in MEKi-treated tumors (Fig. 3d). In contrast, GATA6 low tumors, which showed relatively low expression of pSTAT1, exhibited minimal or no expression of MHCI, CD8, or Cl casp3 upon MEKi treatment (Fig. S3c).

To confirm the role of GATA6 in MEKi-induced anti-tumor responses in the 110299 model, 110299 AID-GATA6 cells and 110299 WT control cells were transplanted, and 5-Ph-IAA (auxin) was administered to degrade GATA6 protein only after tumor formation (200–400 mm³) to ensure that GATA6 degradation did not interfere with tumor development or the immune microenvironment before MEKi treatment (Fig. 3e). GATA6 levels were assessed at different time points after 5-Ph-IAA treatment, and GATA6 expression was notably suppressed by more than 50% after 2 days of treatment (Fig. S3d). MEKi treatment was initiated 3 days after GATA6 degradation (Fig. 3e), and strikingly, the MEKi-induced tumor suppression was completely abolished upon GATA6 degradation (Fig. 3f). Tumors were examined histologically, and significant GATA6 degradation in the tumors was confirmed (Fig. 3g). Notably, MEKi-induced increases in MHCI, CD8, Cl casp3, and pSTAT1 were entirely abolished in the GATA6-degraded model of 110299 AID-GATA6 tumors (Fig. 3h), indicating that GATA6 largely contributes to the MEKi-induced anti-tumor responses. It is noteworthy that 110299 WT tumors treated with 5-Ph-IAA showed no difference in tumor growth (Fig. S3e).

In addition to CD8+ cells, CD4+ cell infiltration was also enhanced in 110299 AID-GATA6 tumors following MEKi treatment, although GATA6 levels remained unchanged (Fig. S3f). Strikingly, MEKi-induced CD4+ cell infiltration persisted despite GATA6 degradation (Fig. S3f), indicating that the GATA6-dependent MEKi-induced tumor inhibition is not mediated by CD4+ cells. These findings highlight a specific role for GATA6 in mediating MEKi-induced anti-tumor cytotoxic responses via the IFNγ-MHCI axis.

MEKi promotes tumor MHCI expression in GATA6+ PDAC subtype with cell state transition in patient-derived xenografts
While MEKi promotes MHCI upregulation, our previous findings revealed an enrichment of EMT-associated features in long-term MEKi-treated cells derived from CKP tumors35, irrespective of subtype, as indicated by transcriptomic profiling. Given that GATA6 is a known suppressor of EMT in PDAC cells, MEKi may drive tumor cell state transitions by downregulating GATA6 expression. Consistently, analysis of orthotopic tumors in the GATA6 high 110299 model also confirmed a reduction in GATA6 expression following MEKi treatment (Fig. S4a).

To confirm the reduction of epithelial features following MEKi treatment in a spontaneous mouse model that closely recapitulates PDAC tumor heterogeneity, CKP mice, which develop aggressive endogenous PDAC at 4–6 weeks of age, were treated with MEKi until the study endpoint (Fig. S4b). MEKi-treated tumors exhibited pronounced shrinkage during the first 2 weeks of treatment compared to vehicle controls (Fig. S4c). However, rapid tumor recurrence was observed in all MEKi-treated mice approximately 3 weeks after treatment (Fig. S4c). Notably, E-cadherin expression was significantly reduced in recurrent tumors following MEKi treatment (Fig. S4d), indicating a loss of epithelial lineage features.

To validate these findings in a clinically relevant setting, we analyzed GATA6 and MHCI expression levels in 15 distinct patient-derived xenografts (PDXs) from the CAM-PaC (Integrative Analysis of Gene Functions in Cellular and Animal Models of Pancreatic Cancer) cohort37 (Fig. 4a). Based on baseline GATA6 expression in tumors without MEKi treatment, the 15 PDXs were stratified into GATA6 high (n=7) and GATA6 low (n=8) groups (Fig. S5a). MEKi treatment significantly upregulated MHCI expression in GATA6 high tumors but not in GATA6 low tumors (Fig. 4b), consistent with our previous in vitro findings. Notably, MEKi-treated GATA6 high tumors exhibited a reduction in GATA6 expression (Fig. 4c) alongside an increase in the mesenchymal marker KRT81 (Fig. S5b), suggesting a tumor cell state transition. In contrast, no significant changes were observed in GATA6 low tumors (Fig. S5c). Collectively, these findings suggest that while MEKi enhances MHCI expression in GATA6 high tumor cells, it concurrently promotes a cell state transition by suppressing GATA6 expression not only in murine but also in human PDAC cells.

HDAC inhibitors restored GATA6 expression and promoted MHCI expression
Given the critical role of GATA6 in MEKi-induced anti-tumor immune responses, mitigating MEKi-induced EMT while preserving an immune-amenable GATA6+ tumor cell population is of therapeutic interest. Histone deacetylase inhibitors (HDACi) have been reported to inhibit EMT and enhance anti-tumor cytotoxicity by promoting antigen presentation25,28,38. Thus, we selected a PDX with high GATA6 expression to assess the combination of the MEKi trametinib with the class I HDACi mocetinostat. While MEKi treatment slightly reduced GATA6 levels, the combination with mocetinostat significantly restored GATA6 expression in tumors (Fig. S5d, upper panel). Notably, while MEKi alone increased MHCI expression, this effect was further enhanced upon combination treatment (Fig. S5d, lower panel).

Given the differential therapeutic and toxicity profiles of various HDACi, we next evaluated a panel of class I (mocetinostat, quisinostat, domatinostat) and class II (tasquinimod, LMK235, ricolinostat) HDAC inhibitors, either alone or in combination with MEKi, for their effects on GATA6 expression in the GATA6 high murine PDAC cell line 110299. MEKi treatment significantly reduced GATA6 expression, while the addition of domatinostat largely restored its levels (Fig. 4d). This aligns with previous findings that domatinostat promotes epithelial gene expression in PDAC cells through a BRD4- and MYC-dependent mechanism25. These findings were further confirmed by western blot analysis across eight murine PDAC cell lines, where GATA6 restoration was observed in all GATA6 high cell lines upon combination treatment with MEKi and domatinostat, but not obvious in GATA6 low cell lines (Fig. 4e). We also examined the epithelial marker E-cadherin and the mesenchymal marker N-cadherin. While MEKi consistently induced EMT across all cell lines, its combination with domatinostat effectively reversed this effect in GATA6 high cell lines by restoring E-cadherin and suppressing N-cadherin expression (Fig. S5e). These findings suggest that HDAC inhibition restores GATA6 expression by counteracting MEKi-induced cell state transitions.

In addition to EMT inhibition, previous studies have shown that HDACi promotes anti-tumor cytotoxicity by enhancing antigen presentation. To further investigate this, we assessed the effects of class I (mocetinostat, quisinostat, domatinostat) and class II (tasquinimod, LMK235, ricolinostat) HDACi, alone or in combination with MEKi, on MHCI expression in GATA6 high and GATA6 low murine PDAC cell lines. All HDACi treatments increased surface MHCI expression, with class I HDACi generally inducing a greater effect than class II HDACi (Figs. 4f and S5f, g). Notably, while MEKi-induced MHCI upregulation was restricted to GATA6 high cell lines, HDACi treatment increased MHCI expression also in GATA6 low cell lines (Figs. 4f and S5f, g). Furthermore, combination treatment with MEKi further augmented surface MHCI levels across most of the cell lines, with the most pronounced increase observed for the MEKi and domatinostat combination (Figs. 4f and S5f, g).

These findings suggest that HDACi not only counteract MEKi-induced GATA6 suppression, but also enhance MHCI expression on tumor cells, thereby potentially augmenting anti-tumor immunity.

Combination of MEKi and HDACi promotes tumor-specific anti-tumor cytotoxicity specifically in GATA6 high tumors in vitro
Next, we investigated whether the MEKi- and HDACi-induced upregulation of MHCI expression translates into enhanced anti-tumor T cell cytotoxicity. Given the typically low mutational burden of PDAC, which limits the availability of neoantigens, we utilized an next-generation dual recombination (Cre/Lox; Flp/FRT) mouse model, FKPC2GP, which enables temporally and spatially controlled expression of the model tumor antigen LCMV-gp33 (Fig. 5a)39–41.

To assess tumor-specific cytotoxicity, we established four low-passage FKPC2GP-derived primary cell lines: GP58, GP82, and GP99, which express LCMV-gp33 upon tamoxifen induction in vitro, and GP2838c3, which constitutively expresses LCMV-gp33. Following treatment with MEKi and HDACi, LCMV-gp33+ tumor cells were co-cultured with LCMV-gp33-reactive T cells isolated from the spleens of P14-TCR-Tg mice (Fig. 5b).

Baseline GATA6 expression levels were evaluated across these cell lines, revealing that GP82 and GP2838c3 exhibited higher GATA6 expression compared to GP99 and GP58 (Fig. 5c). Based on these findings, we classified GP82 and GP2838c3 as GATA6 high models and GP99 and GP58 as GATA6 low models for subsequent analyses.

Consistent with our earlier observations, GATA6 high GP82 and GP2838c3 cells exhibited a significant increase in surface MHCI expression following combined MEKi and HDACi treatment (Fig. 5d). MHCI expression was also upregulated by HDACi and combined MEKi and HDACi treatment on GATA6 low GP99 and GP58 cells, although the magnitude of increase was smaller (Fig. 5d).

To evaluate the functional impact of these changes on anti-tumor immunity, all four LCMV-gp33-expressing tumor cells were co-cultured with LCMV-gp33-reactive T cells isolated from the spleens of P14-TCR-Tg mice. Combined MEKi and HDACi treatment led to a marked accumulation of LCMV-gp33-specific T cells, accompanied by prominent tumor cell death in GATA6 high co-cultures (Fig. 5e). Propidium iodide (PI) staining and flow cytometric analysis confirmed that cytotoxicity was significantly elevated in LCMV-gp33-induced GATA6 high cells upon treatment (Fig. 5e).

In addition, we assessed the activation status of LCMV-gp33-reactive T cells within the co-culture system. Notably, GzmB and PD1 expression was significantly increased in T cells co-cultured with GATA6 high tumor cells treated with MEKi and HDACi, further supporting enhanced T cell activation and tumor-specific cytotoxicity (Fig. 5e). Notably, similar trends could also be observed in GATA6 low co-cultures, although the cytotoxic effects were less prominent than in the GATA6 high co-cultures (Fig. 5f). Together, these findings demonstrate that combined MEKi and HDACi treatment induces MHCI upregulation in PDAC cells, which translates into enhanced tumor-specific T cell cytotoxicity.

MEKi and HDACi combination treatment enhances CD8+ T cell-mediated tumor suppression in orthotopic mouse model
To validate the anti-tumor cytotoxic effects of combined MEKi and HDACi treatment, we administered the combination therapy in an orthotopic mouse model derived from the GATA6 high cell line 110299 (Fig. S6a). Besides, in order to investigate the involvement of CD8+ T cells in the anti-tumor response elicited by the combination therapy, we pre-treated mice with a CD8-depleting antibody prior to administering the combination regimen (Fig. S6b).

Upon combined MEKi and HDACi treatment, tumor growth was markedly suppressed (Fig. S6c). Despite a slight reduction of GATA6 when compared to control tumors, the high proportion of GATA6+ tumor cells remained responsive to the MEKi and HDACi combination therapy, exhibiting significant upregulation of MHCI, increased CD8+ T cell infiltration, and elevated cl casp3 expression (Fig. S6d). These findings suggest that the MEKi and HDACi combined treatment preserves the GATA6-expressing tumor population, which responds by enhancing MHCI expression, ultimately leading to increased CD8+ T cell-mediated cytotoxicity.

Notably, the tumor-suppressive effect of the MEKi and HDACi combined treatment was fully abrogated in CD8-depleted mice (Fig. S6c). Histological analysis confirmed effective CD8+ T cell depletion following anti-CD8 antibody treatment (Fig. S6d). Importantly, tumor cell GATA6 expression remained largely unchanged, while MHCI expression stayed elevated following MEKi and HDACi combination therapy, regardless of CD8 status, suggesting that MHCI upregulation is independent of CD8+ T cell activity (Fig. S6d). In contrast, increase of Cl casp3 after MEKi and HDACi combination therapy was abolished when CD8+ T cells were depleted, further supporting the critical role of CD8+ T cells in mediating the anti-tumor effects of the combined MEK and HDAC inhibition.

Combination of MEKi and HDACi prolongs survival of CKP mice with restored GATA6 and MHCI expression
We next assessed the efficacy of a combined MEK and HDAC inhibition strategy in a highly aggressive, endogenous PDAC mouse model to reflect better the immunosuppressive, desmoplastic, and heterogenic features of human disease and evaluate its potential clinical relevance. CKP mice, aged 6 weeks and presenting measurable advanced tumors, were treated with or without MEKi and/or HDACi until the experimental endpoint (Fig. 6a). While both monotherapies significantly extended survival compared to vehicle-treated controls, the combination treatment further enhanced survival relative to either monotherapy (Fig. 6b).

Tumor volumes assessed by 3D ultrasound imaging showed only 4 out of 6 mice treated with MEKi and none of HDACi-treated mice exhibited tumor shrinkage (Fig. 6c). In contrast, all mice treated with the combination regimen (n=7) showed a sustained reduction in tumor volume throughout the treatment period (Fig. 6c). Importantly, tumor control was maintained in all combination-treated mice at the experimental endpoint, and these animals were euthanized due to weight loss rather than tumor progression (Fig. S7a). Tumor volume changes could not be evaluated in 3 control mice due to their short survival time (less than 1 week after study entry) (Fig. 6c).

To investigate the mechanisms underlying the survival benefit of combination therapy, we compared the immune-related transcriptomic profiles of CKP tumors treated with or without MEKi and/or HDACi using the NanoString PanCancer Immune Profiling Panel. Gene set variation analysis (GSVA) revealed that MEKi alone induced the expression of several immune-related gene sets, including antigen receptor-mediated signaling, T cell receptor signaling, type II interferon activation, T cell activation, and cytotoxicity (Fig. 6d), suggesting its role in promoting T cell-mediated immunity. HDACi alone had a more subtle effect on the tumor immune microenvironment, inducing pathways only related to antigen processing and type II interferon regulation (Fig. 6d). Notably, combined MEKi and HDACi treatment activated a broader range of immune pathways, including those enriched in both monotherapies (Fig. 6d).

To further elucidate the differential effects of MEKi alone versus combination treatment, we compared the immune-related transcriptomic profiles and found that the combination therapy significantly upregulated genes associated with T cell function and antigen presentation (Fig. S7b, c). Additionally, the combination group exhibited higher scores in pathways related to antigenicity, such as MHC and antigen processing (Fig. S7b, c), suggesting that combined MEKi and HDACi enhances tumor antigenicity compared to MEKi alone.

We next assessed GATA6 expression in CKP tumors. Notably, GATA6 expression, which was suppressed in MEKi-treated tumors, was restored following combination treatment with domatinostat (Fig. 6e), suggesting that HDACi mitigated the MEKi-induced loss of classical-type tumor cells.

We then performed multiplex immunofluorescence (mIF) staining for GATA6 and MHCI in CKP tumors. Strong co-expression of these two molecules was observed, particularly in the combination treatment group (Fig. 6f). Quantitative analysis revealed that, upon MEKi treatment alone, the majority of GATA6+ tumor cells were also MHCI+, though GATA6 expression was significantly reduced. Importantly, combination treatment with HDACi significantly restored GATA6+ tumors, with the majority remaining MHCI+ (Fig. 6f, right panel). These findings suggest that HDACi suppresses the MEKi-induced transition and preserves the GATA6+ classical cell state of tumor cells, which are responsive to MEKi with proficient MHCI upregulation. This mechanism enhances the overall antigenicity of the tumors, thereby promoting anti-tumor immune responses.

Enhanced tumor MHCI expression induced by combined MEK and HDAC inhibition is associated with increased cytotoxic T-cell infiltration and tumor apoptosis in vivo
We next examined the effect of treatment on T cell-mediated anti-tumor activity. IHC analysis revealed a significant increase in infiltrating CD8+ T cells in tumors treated with the combination regimen compared to the other groups (Fig. 7a). Notably, the T cell activation marker PD1 and the cytotoxic marker GzmB were both significantly elevated in combination-treated tumors (Fig. 7a). The enhanced anti-tumor cytotoxicity observed with combination treatment was further supported by increased Cl casp3 levels, with no significant changes seen in the single treatment (Fig. 7b).

We validated the increased T cell-mediated cytotoxicity in combination-treated tumors by performing mIF for MHCI, GzmB, Cl casp3, and PanCK as a tumor cell marker, along with computational spatial analysis. In tumor regions with high MHCI expression, we observed abundant GzmB+ cells and increased Cl casp3 expression. In contrast, regions with low MHCI expression showed reduced levels of these markers (Fig. 7c). Co-expression analysis revealed that MHCI+ tumor cells significantly expressed more Cl casp3 compared to MHCI− tumor cells (Fig. 7d). Additionally, spatial analysis demonstrated that a higher number of GzmB+ cells were in close proximity (<150 μm radical distance) to MHCI+ Cl casp3+ cells compared to MHCI− Cl casp3+ cells (Fig. 7e), supporting the critical role of MHCI in tumor immunogenicity and anti-tumor cytotoxicity in PDAC.

Our results demonstrate that MEKi induces GATA6-dependent MHCI upregulation in tumor cells. However, this effect is diminished by a treatment-induced transition to a different cell state, resulting in the loss of GATA6 expression. Combining MEKi with HDACi preserves the GATA6+ tumor cell population, leading to further enhancement of MHCI expression and increased anti-tumor cytotoxicity (Fig. 8).

Methods
Ethics statement
All animal experiments were conducted in accordance with local, national, and European regulations governing the use of laboratory animals. Experimental procedures were approved by the competent animal welfare authorities and performed in compliance with the German Animal Welfare Act (Tierschutzgesetz), the European Directive 2010/63/EU, and the guidelines of the Federation of European Laboratory Animal Science Associations (FELASA).

Animal studies were carried out at Klinikum rechts der Isar, Technical University of Munich, and University Hospital Essen. Experiments performed in Munich were approved from Regierung von Oberbayern, license number was 55.2-1-54-2532-126-2014. Experiments performed in Essen were approved by the Landesamt für Verbraucherschutz und Ernährung (LAVE) Nordrhein-Westfalen. License number for orthotopic mouse models was 81-02.04.2020.A316. License number for patient-derived xenografts models was 84-02.04.2017.A054. License number for CKP was 84-02.04.2017.A315.

According to the approved protocols, the maximal permitted tumor burden was defined as a tumor volume not exceeding 1000 mm³ or 20% of the body weight. Tumor growth was monitored regularly by ultrasound or MRI, depending on the model. Humane endpoints were predefined, and mice were euthanized when tumors approached the maximal allowed size, or earlier if animals exhibited signs of distress, ascites, or body weight loss exceeding 20%.

Due to the interval-based nature of imaging-based tumor monitoring, in rare cases, tumors were found to have exceeded the pre-defined maximal volume at the final measurement. In these instances, animals were euthanized immediately upon detection, and no procedures were performed beyond the approved humane endpoints. Overall, the study was conducted in strict accordance with the approved animal

The use of human PDAC patient samples in this study was approved by the relevant institutional research ethics boards. For the University Health Network (UHN) cohort, resected treatment-naive stage I/II PDAC specimens were obtained through the UHN Biospecimens Program, with approval from the University Health Network Research Ethics Board (approval no. 17-6106). The Essen cohort represents a retrospective study conducted in accordance with the recommendations of the local ethics committee of the Medical Faculty of the University of Duisburg-Essen (approval no. 17-7340-BO). Clinical data were retrieved from archived records and electronic health records in compliance with institutional and ethical regulations. For multiplexed spatial imaging analyses, additional PDAC tissue microarray cores were obtained from the Institute of Pathology, Technical University of Munich, under approval from the local ethics committee (ethical vote: 403/17S).

Written informed consent was obtained from all patients, and the study was conducted in accordance with all relevant local and international ethical guidelines and regulations.

Cohort description
Immunohistochemical staining of GATA6 and stromal/immune markers was performed in a previously described15 tissue microarray (TMA) from PDAC patient samples. In brief, the TMA includes 143 resectable tumor specimens from treatment-naive patients with stage I/II PDAC. The information of the patient cohort, including age and sex of subjects, has been published29. Resectable tumors were obtained from the UHN Biospecimens Program. Patient samples were mostly accrued at Princess Margaret Cancer Centre at the University Health Network (Toronto, Canada) as reported previously29.

Multiplexed histological staining for spatial interaction analysis of GATA6+ tumor cells, and also Phenocycler analysis, was performed in the “Essen cohort,” which is a retrospective study that was carried out according to the recommendations of the local ethics committee of the Medical Faculty of the University of Duisburg-Essen. Clinical data were obtained from archives and electronic health records. In this exploratory retrospective study, a cohort of 54 patients that had undergone pancreatic resection with a final histopathologic diagnosis of human PDAC between March 2006 and February 2016 was used.

6 cases of PDAC primary tumors (four obtained from male sex patients and two from female sex patients), analyzed by the highly multiplex spatial imaging with Phenocycler, were available as TMA cores. Here, we used a tissue microarray compiled at the Institute of Pathology, Technical University of Munich, consisting of primary resected PDAC, resected between April 2008 and May 2020. The clinicopathological data of the patients are shown in Supplementary Table 1.

Public datasets and bioinformatic analysis
Bulk RNA-sequencing dataset (GSE93326)30 and single-cell RNA-sequencing dataset (GSE212966)56 were used to analyze the association of GATA6 expression in tumor with TME. GSE93326 dataset contains 65 pairs of tumor epithelium and stroma LCM samples. “DESeq2” R package was used to identify the Differential Expression Genes (DEGs) between GATA6 high and GATA6 low samples. GSE212966 consists of 6 PDAC and 6 adjacent noncancerous resection specimens. We used the 6 PDAC samples data to do analysis. “Seurat” R package was used to standardize the expression of filtered samples and identify the top 2000 genes with the most noticeable difference between cells. Cells were filtered out with the threshold of the ratio of mitochondrial genes ≤15%. Genes expressed in >3 cells and cells with at least 200 genes were retained. An integrated dataset was created by using “Harmony” R package. The integrated dataset then proceeded with principal component analysis (PCA), and the dimensions of the top 30 PCs were reduced by the tSNE and UMAP algorithm to obtain principal clusters. FindAllMarkers function in “Seurat” R package was used to identify marker genes of each cluster. Finally, the cells were annotated according to marker genes expression. DEGs between GATA6+ tumor cells and GATA6− tumor cells were analyzed using the Wilcoxon rank sum test with Bonferroni correction included in FindMarkers function in “Seurat” R package. “CellChat” R package and CellChatDB database were used to infer the cellular interactions among GATA6+/− tumor cells and CD20+ B cells, CD8+ T cells, CD4+ T cells, and FOXP3+ T cells by using the expression of known ligand-receptor pairs and identifying the changes in cellular interaction, the netVisual_circle function was utilized for evaluating the strength of cellular interaction networks among cell types. Gene Set Enrichment Analysis (GSEA) was conducted using the GSEA function from the “ClusterProfiler” R package. The gene chip annotation file Human Gene Symbol with_Remapping_MSigDB.v7.4.chip was utilized, and genes were collapsed and remapped to gene symbols. The analysis incorporated gene sets from Hallmarks (h.all.v7.5.1), KEGG (2.cp.kegg.v7.5.1), and GO (c5.go.v7.5.1).

Phenocycler multiplex tissue staining and imaging
Formalin-fixed paraffin-embedded (FFPE) tissue was cut at 5 µm thickness and mounted onto poly-lysine-coated coverslip (for resected tumors); while for TMA cases, FFPE tissue was cut at 5 µm thickness and mounted onto microscope slides (Superfrost®). The tissues were deparaffinized and rehydrated, and heat-induced epitope retrieval was performed at 97°C for 10 min. The tissue was stained with an antibody cocktail with 27 antibodies or 40 antibodies to a volume of 100 µl overnight at 4°C in a sealed humidity chamber on a shaker. After multiple fixation steps using 1.6% paraformaldehyde, 100% methanol, and BS3 (Thermo Fisher Scientific, #21580), the coverslip was mounted onto a custom-made acrylic plate (Bayview Plastic Solutions). Imaging was performed with a Keyence BZ-X710 inverted fluorescence microscope, or PhenoImager FUSION microscope (Akoya Biosciences). For resected tumor tissues imaged by Keyence, four regions of interest (ROIs) were selected from the resected tissues for analysis; while for TMA imaged by FUSION, the whole section of all tissue cores were imaged. Antibody information, light exposure times and the arrangement of cycles are shown in Supplementary Dataset 6.

Data processing of CODEX and Phenocycler/FUSION images
Raw TIFF image files were processed using the CODEX Toolkit (github.com/nolanlab/CODEX). After processing, the staining quality for each antibody was visually assessed in each tissue microarray spot, and cell segmentation was performed using the DAPI nuclear stain. Marker expression was quantified, and single-cell data were saved as FCS files, which were then imported into CellEngine (cellengine.com) for cleanup gating. After that, the data was analyzed by HALO for quantification and spatial interaction analysis. For CODEX, four ROIs were selected from each tumor tissue. Average of the four ROIs was shown and represent one patient.

Multiplexed immunofluorescent (IF) histological staining
Multiplexed IF was conducted using the Opal multiplex system (Akoya Biosciences, MA) in accordance with manufacturer’s instructions. In brief, FFPE sections were deparaffinized, then fixed with 4% paraformaldehyde, and the antigens were retrieved using tris/EDTA (pH 9) to induce epitope retrieval by heat-induced epitope retrieval. Each section was put through multiple rounds of staining; each included endogenous peroxidase blocking and protein blocking, as well as primary antibody and the corresponding secondary horseradish peroxidase-conjugated polymer (Zytomed Systems, Germany, or Akoya Biosciences). Tyramide signal amplification was used to bind each horseradish peroxidase-conjugated polymer with different fluorophores. To remove antibodies before the next round of staining, additional antigen retrieval in heated Tris/EDTA (pH 9) was performed. Sections were counterstained with DAPI (Vectorlab) after all sequential staining reactions. The sequential multiplexed staining protocol is shown in Supplementary Table 2. With 10x objective magnification, slides were scanned and digitalized by Zeiss Axio Scan. Z.1, ZEN Blue software version 3.1 (Carl Zeiss AG, Germany). Quantification of individual and/or co-expressing markers, and spatial interaction analysis in the mIF images was performed with HALO version 3.2 (Indica Labs).

Genetic mouse strains and models
The study was not applied to only one gender. Mice (both sexes, aged 6–12 weeks) were randomly assigned to experimental groups. No specific sex or age selection was made. Ptf1awt/Cre;Kraswt/LSL-G12D;Trp53tm1.1Dgk (CKP) mice spontaneously develop PDAC at 6 weeks40,57. FKPC2GP mice were generated by crossing Ptf1awt/Flp;Kraswt/FSF-G12D;Trp53tm1.1Dgk (FKP) mice to Gt(ROSA)26Sortm3(CAG-Cre/ERT2)Dsa(R26FSF-CAG−CreERT2) and Gt(ROSA)26SortmloxP-STOP-loxP-GP-IRES-YFP(R26LSL-GP) strains39–41,58. For treatment experiments, mice were randomized. None of the mice with the appropriate genotype were excluded from this study.

Establishment of orthotopic mouse models
For the generation of orthotopic PDAC tumors in mouse based on CKP-derived cell lines (60590 and 110299) and AID system cells (110299 AID-GATA6 and 110299 WT), 5×103 cells were resuspended in 30 µL of a 1:1 dilution of Matrigel® (Corning) and cold plain medium and injected into the pancreas of C57Bl/6J (B6J) mice using insulin syringes (BD micro-fine 30 Gauge) under the guidance of ultrasonic imaging (visualsonic vevo 2100, FUJIFILM Sonosite, Canada). The injection was considered successful by the circular appearance of encapsulated cell suspension without signs of leakage. After orthotopic transplantation, tumor volume of tumor-bearing mice was monitored by 3D mode of ultrasound imaging once a week59.

Establishment of patient-derived xenografts
Fresh tumor fragments were transplanted subcutaneously (s.c.) into the left flank of anaesthetized NOD scid gamma mice. Mice were maintained under sterile and controlled conditions (22°C, 50% relative humidity, 12 h light–dark cycle, autoclaved food and bedding, acidified drinking water). Tumor growth was measured twice weekly, and tumors were routinely passaged at TV=1000 mm³. Xenograft material was snap frozen and stored at −80°C or processed to FFPE blocks.

In vivo treatment of mice
For all orthotopic PDAC models, treatment was initiated when tumors reached a volume of 200–400 mm³. Mice were treated with either vehicle control (2% DMSO+40% PEG300+5% Tween-80+53% ddH 2O), refametinib (RDEA119, BAY86-9766, 25 mg/kg), domatinostat (4SC-202, 40 mg/kg), or a combination of refametinib and domatinostat. Drugs were administered by oral once daily for 5 consecutive days, followed by a 2-day treatment break, in repeated cycles. For mice bearing orthotopic tumors derived from the AID-GATA6 system cell lines, 5-Ph-IAA (auxin, 20 mg/kg) was administered intraperitoneally (i.p) once daily, starting 3 days before the initiation of refametinib treatment and continuing throughout the treatment period. For CD8⁺ T cell depletion experiments, anti-CD8 antibody (100 µg/mouse, i.p.) was given once daily for 2 consecutive days, followed by a 3-day rest period. Afterward, combination therapy with refametinib and domatinostat was initiated as described above. Tumor volume in orthotopic models was monitored weekly using ultrasound starting 2 weeks after tumor cell implantation.

For PDX models, treatment was initiated when tumor volume reached approximately 200 mm³. Mice received either vehicle control (2% DMSO+40% PEG300+5% Tween-80+53% ddH 2O), trametinib (GSK1120212, 1 mg/kg), mocetinostat (MG0103, 40 mg/kg), or the combination of trametinib and mocetinostat. Drugs were administered by oral once daily for 5 consecutive days, followed by a 2-day treatment break, in repeated cycles for 4 weeks.

For CKP mice, treatment started at 6 weeks of age when the tumor diameter of the tumor was approximately 5 mm with vehicle control, trametinib, domatinostat, or the combination of trametinib and domatinostat. Drugs were administered the same as PDX models. MRI was performed according to relevant guidelines and regulations60,61. Tumor burden was measured by ultrasound weekly from 5 weeks old. Mice were euthanized when tumors reached a volume >1000 mm³, exhibited >10% body weight loss, developed ascites, or showed signs of distress. Tumors were harvested for further experiments.

Immune mRNA profiling
All procedures related to mRNA quantification, including sample preparation, hybridization, detection, and scanning, were carried out by NanoString Technologies for PanCancer Immune Profiling codesets (NanoString Technologies). Normalized data were log 2-transformed and then used as input for differential expression clustering and for heat maps. For the PanCancer Immune codeset, genes previously shown to be characteristic of various immune cell populations were used to measure the abundance of these populations with adjusted p value <0.05, Benjamini-Yekutieli false discovery rate (FDR) <0.1. nSolver Software version 4 (Nanostring Technologies) was used for analysis. Subsequent GSVA performed with R Studio with gene expression data after processing with nSolver.

Immunohistochemistry
All IHC experiments were conducted on FFPE sections. For IHC staining of GATA6, CD3, CD8, CD20, CD4, CD68, and FoxP3 on a TMA of n=143 human PDA tissues, digital quantification by QuPath62 and data processing within our in-hour pipeline (“HOURGLASS”) were reported previously29,63. In brief, GATA6 expression was quantified within malignant epithelia, based on manual annotations drawn on every core by a trained pathologist. CD3+ and CD8+ TILs were manually counted by a trained pathologist and normalized by total tissue area. CD4+ and FoxP3+ cells were digitally quantified and normalized by total tissue area. CD20+ and FoxP3+ cells were digitally quantified and normalized to the CK19− area fraction of the tissue area, since these populations maintained an exclusive localization to stromal regions.

For all the remaining IHC stainings performed in the present study, heat-induced epitope retrieval was performed with Tris/EDTA (pH 9). A serum-free protein blocking solution (Zytomed System) was used to block the slides, followed by incubation with primary antibodies (Supplementary Table 3) for 1 h at room temperature, and secondary antibodies for 30 min at room temperature, and then DAB chromogen development was performed. As for the staining of H-2Db, a mouse-on-mouse (MOM) immunodetection kit (BMK-2202, Vector Laboratories) was used. Counter-staining with hematoxylin, dehydrating, and mounting the slides followed.

With a 10× objective magnification, the slides were scanned and digitalized by Zeiss Axio Scanner Z.1 (Carl Zeiss AG, Germany). A quantitative analysis of the percentage of positive IHC-stained cells was conducted by Definiens version 2.6 (Definiens AG, Germany). Nuclei staining (hematoxylin) detected by the software was used to calculate the number of cells in the entire tissue section.

Cell culture and treatment
Primary murine PDAC cell lines and GP cell lines5,39 had been derived previously from corresponding tumor pieces of CKP and FKPC2GP mice through incubation in DMEM high-glucose medium (Thermo Fisher Scientific) supplemented with 10% fetal bovine serum (Thermo Fisher Scientific) until proliferating tumor cells could be established. 2838c3 and 6694C2 KPCY cell lines were purchased from Kerafast (CAT# EUP013-FP, Boston, USA). The long-term trametinib-treated murine PDAC cell lines were established by treatment with increasing doses of trametinib (GSK1120212) until 100x of the cells’ initial IC50 and maintained in 10% FBS-supplemented DMEM with corresponding doses of trametinib.

CKP cells, both parental and long-term trametinib-treated, were seeded in 6-well plates (200 K/well) and adhered 8 h. Cells were then treated with DMSO (concentration-matched vehicle control), or trametinib (78.125 nmol/L), or domatinostat (312.5 nmol/L), or the combination of trametinib (78.125 nmol/L) and domatinostat (312.5 nmol/L) for 72 h.

For auxin-induced degradation, CKP cells (110299) were incubated with or without 1 µM 5-Ph-IAA (BioAcademia, Japan) in culture medium for different time points.

BrdU Incorporation Assay
Cell proliferation was measured using the BrdU Cell Proliferation Assay Kit (Sigma-Aldrich, QIA58) according to the manufacturer’s instructions. Briefly, 1×10⁵ cells/mL were seeded in 96-well plates (100 µL/well) and allowed to attach for 24 h. BrdU (1:2000) was added (20 µL/well), and cells were incubated for another 24 h at 37°C. Blank (medium only) and background (cells without BrdU) controls were included. After fixation and denaturation, wells were incubated sequentially with anti-BrdU antibody (1:100, 1 h), HRP-conjugated secondary antibody (1:1000, 30 min), and substrate solution (15 min, dark). After adding stop solution, absorbance was measured at 450 nm with a reference of 595 nm using a Tecan plate reader.

Generation of GATA6 knockout cell lines
Modified from lentiCRISPR v2 (Plasmid #52961, Addgene), pLentiCRISPR-V2.1-GFP contains inserts Cas9, sgRNA scaffold, GFP, and Puromycin resistance. After inserted with sgRNA targeting Gata6 (sgGata6) or nontargeting control sequence (sgNT), pLentiCRISPR-V2.1-GFP-sgGata6 were used for CRISPR-Cas9 genome editing of KPCY 2838c3 cells. Modified from lentiGuide-Puro (Plasmid #52963, Addgene), plentiGuide-Puro-BFP contains the inserts sgRNA scaffold, BFP, and Puromycin resistance. After inserted with sgRNA targeting Cas9 (sgCas9), plentiGuide-Puro-BFP-sgCas9 was used for Cas9 removal. Modifided from pCDH-EF1 (Plasmid #72266, Addgene), pCDH-EF1a-BFP-GP33-GP41 contains inserts BFP and GP33-GP41 peptide, which is H-2Db-restricted epitope generated from the lymphocytic choriomeningitis virus (LCMV). The gRNA sequences are as follows: sgCas9-1 CAGATAGATCAGCCGCAGGT; sgGATA6_1 TACGTGC CCACCACGCGCGT; sgGATA6_2 CCGGGAGTGGAGCTCCCGCG; sgGATA6_3 AGGGCGAGTAGGTCGGGTGA; sgGATA6_4 TCCGCCGACAGCC CCCCGTA. Specifically, 2838c3 cell lines were first transduced with pLentiCRISPR-V2.1-GFP-sgRNA targeting Gata6 (sgGata6) or non-targeting control sequence (sgNT) and then transduced with plentiGuide-Puro-BFP-sgRNA targeting Cas9 (sgCas9) 7 days after first transduction, followed by sorting of GFP and BFP double-positive cells. After a 7-day recovery, the cells are ready for the following functionality experiments. To express GP33-41, 2838c3 cell lines were transduced with pCDH-EF1a-BFP-GP33-GP41, followed by sorting of BFP-positive cells and 4 days’ recovery. The cells are then ready for the following functionality experiments.

Western blot analysis
Cell sort tissues were lysed in RIPA buffer (Cell Signaling Technology, 9806) supplemented with protease inhibitor (Sigma-Aldrich, 4693124001) and phosphatase inhibitor (Sigma-Aldrich, 4906837001). Lysates were incubated on ice for 30 min and then centrifuged at 12,000×g for 15 min at 4°C. Protein concentration was determined using the Pierce BCA Protein Assay Kit (Thermo Fisher, 23225). Equal amounts of protein (20 µg) were loaded onto SDS-PAGE gels (8–12%) and transferred to PVDF membranes. After blocking with 5% BSA in TBST for 1 h at room temperature, membranes were incubated overnight at 4°C with primary antibodies: anti-GATA6 (Abcam, ab175349, 1:1000), anti-GAPDH (Cell Signaling Technology, 9098, 1:1000), anti-pSTAT3 (Cell Signaling Technology, 9145S, 1:1000), Vinculin (Cell Signaling Technologies, 13901S, 1:1000), β-actin (Abcam, ab8227, 1:1000), ERK1/2 (Cell Signaling Technology, 4695T, 1:1000), phospho-ERK1/2 (pT202/pT204, Cell Signaling Technology, 4376S, 1:1000), E-Cadherin (Cell Signaling Technology, 3195S, 1:1000), and N-Cadherin (Abcam, ab76057, 1:1000). HRP-conjugated secondary antibodies were applied for 1 h at room temperature. Blots were developed using WesternBright ECL HRP Substrate (Advansta, K-12045-D20), followed by imaging with a chemiluminescent imaging system.

Endogenous knock-in of AID tag
A homology-directed repair (HDR) template containing homology arm (HA) homologs to the sequences upstream and downstream of the gata6 start codon was cloned. HAs were PCR-amplified using murine primary PDAC cell line (110299) genomic DNA as template and cloned into pJET (Thermo Fisher Scientific) flanking the Blast-P2A-V5-AID cassette to generate pJET_gata6_N-AID_HDR. Two sgRNAs targeting around the gata6 start codon were cloned into PX458 (Addgene #48138) to obtain PX458_gata6_sgRNA1 and PX458_ gata6_sgRNA2. The PAM sites of the two sgRNAs were mutated in the HDR using the primers for the HA PCR amplification.

To obtain stable cell lines expressing endogenous GATA6 with an AID tag at the N-terminal, Murine primary PDAC cells (110299) were grown in 6-well dishes and transfected with 3 µg of each PX458_gata6_sgRNA1 or PX458_gata6_sgRNA2 and pJET_gata6_N-AID_HDR plasmids using polyethylenimine (PEI). After 72 h of transfection, cells were trypsinized, reseeded into several 15 cm dishes with various dilution and selected with 15 µg/ml Blasticidin. After 7 days of selection, colonies were picked and transferred to 24-well plates. Individual clones were evaluated using genomic PCR and western blot. PCR products from some clones were purified using the GeneJET Gel Extraction kit (Thermo Fisher Scientific) and sent for Sanger sequencing (LGC Genomics). For TIR1F74G expression in 110299 AID-GATA6 cells, TIR1F74G and eGFP were cloned into pRRLSin.cPPT.SFFV-IRES-Hygro vector to obtain pRRL-hygro-TIR1(F74G)-T2A-eGFP. 110299 AID-GATA6 cells were then lentivirally transduced with pRRL-hygro-TIR1(F74G)-T2A-eGFP vector and selected with 500 µg/ml hygromycin (InvivoGen) for a week.

RNA sequencing data
Generated as described35, available in the GEO database under accession code GEO: GSE146348.

Hierarchical clustering
Hierarchical clustering of parental vs. long-term trametinib-treated CKP cells based on PDAssigner genes or PDAC subtype-associated genes defined by Bailey et al.44 and Moffitt et al.64. Benjamini-Hochberg adjusted p value <0.01; log 2 fold change >1 or log 2 fold change <−1.

Gene set enrichment analysis (GSEA)
GSEA was performed using default settings of Broad Institute algorithm, and gene set permutation on the Hallmarks and KEGG gene sets. Benjamini-Hochberg FDR q value <0.05, NES >±1.5.

Flow cytometric analysis
For cell surface MHCI and PD1 expression, cells were washed with washing buffer (2% FBS in PBS) and then incubated with antibodies: MHCI (Biolegend, 111513, lot: B351983); PD1 (Biolegend, 135213), or an equal amount of corresponding isotype control. For intracellular TNF, GzmB, and GATA6 expression, cells were fixed with 4% paraformaldehyde for 10 min at 37°C. After washing twice with washing buffer, cells were permeabilized with 0.1% Saponin for 20 min and then stained with antibodies: TNFa (Biolegend, 506306, lot: B290019); GzmB (BioLegend, 515406, lot: B301362); GATA6 (Cell Signaling Technology, 26452S, lot: 4) and corresponding isotype. Cells were then washed, resuspended, and subjected to analysis. Expression of corresponding molecules of 10,000 viable cells was analyzed by flow cytometry (FACSCelasta, FACSDiva software, version 5.0; BD Biosciences) as mean fluorescence intensity. Raw data were analyzed using FlowJo software version 7.5.5 (Tree Star Inc., Ashland, OR).

Isolation of T cells from P14-TCR-Tg mice
Spleens were collected from P14-TCR-Tg mice. Afterwards, they were homogenized freshly, then lysed in Ammonium-Chloride-Potassium lysis buffer. In accordance with manufacturer’s instructions, T cells were negatively selected by MACS (Miltenyi Biotec). After isolation, T cells were labeled with 5 µM CFSE (Thermofisher) and resuspended in 10% FBS-supplemented RPMI medium in prestimulated with 20 ng/ml IL-2 (Peprotech) for 1 h, before co-culture with GP cell lines.

Co-culture of LCMV-gp33-reactive T cells and GP cells
After treatment with or without tamoxifen to induce LCMV-gp33 expression for 2 days, GP cells were treated with the same four groups of treatments for CKP cells for 72 h. Following that, the cells were harvested and seeded in 6-well plates containing 10% FBS-supplemented DMEM and were ready for co-culture. LCMV-gp33-reactive T cells were subsequently isolated and then cultured in DMEM supplemented with 10% FBS for 48 h with or without GP cell lines at a ratio of 8:1. The cells were then photographed under a microscope, then harvested for propidium iodide (PI) staining to determine whether they have anti-tumor cytotoxicity. Furthermore, GzmB, TNFa, and PD1 were stained to assess the activity of T-cells.

Statistical analysis
All statistics were performed using GraphPad Prism version 8.0 (GraphPad Software, La Jolla, CA) or R software (version 4.1.3). Survival data were analyzed by two-sided log-rank (Mantel–Cox) test, while correlation analysis was by Spearman’s rank correlation coefficient. The Chi-square test was used to assess the independence between two categorical variables. Unpaired two-tailed Mann–Whitney test was applied for non-normally distributed data comparison between unpaired two groups. For paired two groups, two-tailed Wilcoxon matched-pairs signed rank test was used. For multiple group comparison, One-way ANOVA and Kruskal–Wallis test were used. Data are represented as mean ± S.D. The exact p values are shown in the figures.

Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability
Publicly available RNA sequencing datasets used in this study were obtained from the Gene Expression Omnibus (GEO) database under accession numbers: GSE146348, GSE93326, GSE212966. These datasets were generated by independent studies and were not produced as part of this publication. Source data are provided with this paper.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors present a manuscript that assesses the ability of MEK inhibitors to alter MHCI levels depending upon the level of GATA6 in pancreatic cancer cell lines. The present a convincing MFI analysis of two high GAT6 versus two low GAT6 cell lines to demonstrate only the high GATA6 cell lines increase MHCI when exposed to MEKi. As MHCI is critical for immune recognition and/or evasion, mechanisms that can increase MHCI expression are critical for improving immunotherapy. The authors further combine MEKi with HDACi, which shows increased survival of CKP pancreatic cancer bearing mice. The mechanisms behind the HDACi is not well laid out or tested, leaving a GAP in the understanding and applicability of this combination therapy. This is a highly correlative study that provides novel insight into the roles of MEK and GATA6 in pancreatic cancer, yet multiple moderate concerns decrease the enthusiasm for this manuscript.\nConcerns:\nWriting issues:\n1.) The introduction and some of the discussion are not well written and lack clarity for multiple statements; they are generally broad and do not provide the reader with accurate/specific interpretations from the cited references. Especially with respect to previous studies on GATA6 in the introduction. Additionally, the First two lines in the abstract are too broad, unsupported, and should be rewritten.\n2.) Multiple small grammatical issues are detected throughout the manuscript.\n3.) Methods for ultrasound imaging of the tumors is not provided.\n4.) GP99 cells should not be portrayed as GATA6- as this implies negative; in reality they do express low levels (fig3d). It is suggested to use the term GATA6lo. It is also suggested that a GATA6 knockout be test as cancer cells are highly plastic and the ability to reexpress GATA6 in lo cells is unclear.\n5.) GATA6 does not “orchestrate” EMT, instead the reference demonstrates that GATA6 is required to maintain epithelial differentiation. The following is therefore quite misleading and should be rewritten : “Recent seminal studies have illuminated the crucial tumor-suppressive role of GATA6 in orchestrating epithelial-to-mesenchymal transition (EMT) and restraining tumor dissemination in PDAC 4-6.”\n6.) The following statement is also without a value “Despite the prognostic and predictive value of GATA6 loss in response to adjuvant chemotherapy 6”, authors should clearly state if this is positive or negative.\n7.) The following statement is also without a value “Histone deacetylase inhibitors (HDACi) are known to affect EMT-like features of cancer cells and tumor-stromal crosstalk in PDAC 18,19”, authors should clearly state if this is positive or negative.\nExperimental issues:\n1.) Baseline proliferation rates should also be shown for each cell line and each treatment.\n2.) It is unclear if the MHCI expression correlates with active proliferation in the cells. As both GATA6 loss and MAPK activity are likely to associate with altered proliferation. The low MHCI expression could be a result cells in an active cell cycle, which would likely not have MHCI on the surface as they have freshly divided. Many of the results are correlative and may not indicate a direct regulation of MHCI.\n3.) Figure 3: Experiments in figure 3f should be performed in the presence of anti-PD1 as a control to determine if this will eliminate the difference in between GP82 and GP99 experiments.\n4.) Figure 5: Figure 5a, body weights (stated in results section) are missing from the figure. Figure 5b,c it is unclear why the mice died if the tumors were decreasing in volume. This should be explored and explained. Figure 5f, the MHCI IHC is very faint if any protein in detectable at all from the image provided and is not very convincing or representative of the quantitative data in 5g. The cellular localization of GATA6 is not well explained and appears to shift from cytoplasmic to nuclear based on the treatment provided. Further fractionation analysis should be provided to quantify.\n5.) Rationale for using the HDACi is limited and not rigorously justified. With the broad spectrum impact of HDAC inhibitors it is unclear how this study will benefit the field. The further lack of consideration for off target effects of the HDAC inhibitors is also concerning; these could have indirect effects that obscure the proposed mechanism.\n6.) Authors fail to describe or differentiate how HDAC and MEK inhibitors directly affect T cells or other stromal cells and the implications that could have for these studies.\n7.) Lack of validation/controls for GATA6 or MEK regulation in response to HDAC inhibitors. Authors should monitor these in response to HDAC inhibition. nuclear versus cytoplasmic ratio of GATA6 in response to each treatment.", "reviewer_2": "Yang and co-authors present new data on the role of GATA6 in regulating immune responsiveness and MHC-I expression in the mouse and human models of PDAC. The study is well-designed and presents compelling evidence derived from genetically engineered cell lines and animal models. The main thrust of the paper is to demonstrate that MEKi increase MHC-I expression and type-I IFN signatures selectively in GATA6 classical subsets of PDAC, whereas GATA6-low EMT PDAC cells evade immune surveillance.\nSeveral issues would be important to address to solidify the overall impact:\n1) The proposed immunological mechanism of MEKi+HDACi is not sufficiently validated. Experiments to reverse the survival advantage by depletion of CD8/CD4 cells or in immunodeficient hosts will be important to exclude non-immune mechanisms of MEKi+HDACi combinatorial activity.\n2) The ex vivo experiments with T cell and PDAC co-cultures should be feasible to model in vivo in the TCR Tg mice. This or other immunogenic models (e.g., Ova/OT-I TCR Tg) cold be used to demonstrate the efficacy of MEKi+HDACi combination.\n3) By the same token, the cellular model of GATA6 downregulation (or alternatively using Dox-shGATA6) should be deployed in vivo to valiadate the effect on tumor growth and MHC-I.\nMinor comments:\n1) please describe how Gata6 hi and lo cell were separated by FACS (Fig.2a).\n2) Figure 3c and 3f are poorly visible. Suggest higher magnification. Controls of MEKi alone should be provided to demonstrate its direct effect on viability. Figure legend does not explain the color in the image- is it CFSE in T cells? Are PI images available or was it done by FACS?\n3) Figures 6 and 7 provide indirect correlations without direct mechanistic evidence. These can be enhanced and replaced with direct results demonstrating the role of T cell immunity in the context of genetic or pharmacological GATA6 modulation.", "reviewer_3": "Yang et al. present evidence that GATA6+ expression in PDAC tumors and cell lines is required for MEK inhibition to promote tumor immunity via increased expression of MHC-I and IFN-gamma. However, long-term MEK inhibition in vivo is ineffective due to lower GATA6 expression and consequently, less antigen presentation. The authors show that HDAC inhibition can prevent GATA6 downregulation in tumors, and thus work synergistically with MEK inhibition. GATA6 expression may serve as a biomarker of responsiveness to MEK+HDAC inhibitor combinations. These findings are intriguing and may have clinical relevance; however, several of the principal findings would be more convincing if supported by more in-depth characterization and by using additional cell lines.\nLarger cohort and alternate analyses needed for data from patient tumors\nThe authors show that GATA6-high patient tumors harbor denser populations of immune cells (both anti-tumor CD8+ T cells and pro-tumor FOXP3+ T cells) compared to GATA6-low tumors. In PDAC, there is a negative correlation between tumor cell density and the densities of many immune cell types (both anti-tumor and pro-tumor). The authors should report whether tumor cellularity per se aligns with leukocyte density. Is there a relationship between tumor cellularity within groups (GATA6-high, GATA6-int, and GATA-6-low)?\nThe GATA6 staining from CODEX in Figure 1D and E appears regional, and may be due to low-quality tissue in that region. Although the PanKRT stain is high throughout, it’s difficult to tell how thresholds were set to detect staining differences. A counter stain for basal-like tumor cells in Supp Figure 1 appears to show no KRT5 staining, consistent with regional tissue quality problems. If KRT5 is not expressed by this tumor, KRT6A, and TP63 are other options. Additionally, a single patient specimen is insufficient for drawing conclusions about intratumoral GATA6 heterogeneity and leukocyte proximity. This data should be supported by simple immunofluorescence co-staining for GATA6, a basal-like marker, and 1 or 2 leukocyte markers in multiple ROIs from ≥5 patient tumors. Tumors with relatively uniform classical-subtype tumor cells and tumors with predominantly basal-like tumor cells should be added as controls.\nThe authors suggest that leukocyte density is related to GATA6 expression and not CLDN18 expression (also considered a classical subtype marker), but the comparison between CLDN18+ and CLDN18neg tumor cells in Supp Figure 1 and their proximity to immune cells is less informative than a comparison between CLDN18+GATA6neg and CLDN18negGATA6+ tumor cells. Statistics are also missing for this data.\nThe relationship between GATA6 expression and the classical subtype is important context, but is not sufficiently developed, especially with the patient tumor analyses. One of the many published subtyping approaches should be applied to patient tumor gene expression data, as well as to individual cells from scRNAseq datasets and LCM tumor regions. Additionally, there are several published scRNA-seq datasets that should be used to identify tumor cell-specific GATA6 expression (like the dataset used for Figure 1C). The Mauer et al. LCMD data is less ideal than scRNAseq datasets for addressing cell-level heterogeneity.\nMore mouse cell lines needed for in vitro mechanism experiments\nThe authors generated cell lines from syngeneic KPC mice. Since these lines were not subcloned, it is surprising that 3 of them show heterogeneous GATA6 expression, and 3 show no GATA6 expression at all (Figure 2A). The authors should genetically validate that these lines are exclusively tumor cells, and these experiments should be done with FACS-sorted GATA6-high and GATA6-low cells from several heterogeneous cell lines. The top histogram in Figure 2A shows an unusual bi-modal expression of GATA6 with many cells on the right y-axis. These data should be inspected for FACS artifacts, and a better example plot should be presented. Additionally, it appears that only 4 of the 6 cell lines are used for the data in Figure 2c (it’s unclear how many were used for Figure 2b). How were these selected? The AID approach in Figure 2e-g more convincingly demonstrates the relationship between MEKi, GATA6, and antigen presentation, but should be performed on ≥3 distinct AID-altered lines. Additionally, more than 2 cell lines should be derived from the FKPC2GP mice to make conclusions that the distinct morphologies from the 2 cell lines match mechanisms related to GATA6 and MEK inhibition. Similarly, the data in Figure 4e appear to be from 2 KPC cell lines, this in vitro experiment should be performed on ≥5 cell lines with statistical comparisons shown.\nThe treatment regimen for the in vivo experiment in Supp Figure 3b is not clear. Did the recurrence occur during MEKi treatment or did MEKi withdrawal prompt the recurrence? Additionally, the authors should provide MR images from the whole cohort data.\nThe Figure 5e figure legend states that the MHC-I and GATA6 staining is from tumor cells but also from “whole tumorous tissue”. This should be clarified. Figure 5g shows the percentage of tumor cells expressing GATA6 and MHC-I (though the y-axis is labelled only GATA6+) but it’s unclear if the bars are superimposed or stacked and statistical comparisons are missing. Figure 6 shows that with the treatment effect (greatest in the MEKi+HDACi combination) there is an increase in the percentage of leukocytes. Is this the percentage among all cells? As with earlier figures, these data would be easier to interpret with cell densities.\nAdditional Comments\nFigure 1 and throughout, text on figure labels (especially axes labels) should be written in common syntax, without underscores and periods, and with appropriate superscripts.\nIn Figure 1 (and throughout) for GSEA results, the authors should indicate which pathways are considered significant – the color and size scales for NES and FDR are difficult to interpret; nominal P values should also be given.\nNumbers are missing from the y-axes of Figure 4b and Figure 4c making it difficult to compare data between the charts. The values for GATA6 expression are similar between the control conditions of GATA6-hi and GATA6-low tumors for Figure 4c. Did GATA6 expression change in vivo or are the y-axes different?", "reviewer_4": "Yang et al. investigated the impact of combined MEK inhibitor (MEKi) and histone deacetylase inhibitor (HDACi) treatment on GATA6-dependent MHCI expression in pancreatic ductal adenocarcinoma (PDAC). The authors showed that MEKi alone increased MHCI expression in GATA6-positive pancreatic cancer cells and triggers epithelial-to-mesenchymal transition that appeared to reduce the effect. The addition of HDACi (domatinostat), preserved the GATA6-positive population and enhanced MHCI expression, resulting in improved immune response and decreased tumor growth. This combination therapy seemed to significantly increase the cytotoxic T-cell infiltration, enhancing anti-tumor responses. However, there appears to be a lack of coherence and consistency in linking the mechanistic insights across clinical data and experimental data. More importantly, it is not clear that MHCI up-regulation is mechanistically important for the anti-tumor immunity upon the MEKi/HDACi combo treatment, while MEKi/HDACi combo treatment might have pleiotropic effects on both tumor and stroma. While the study presents a novel approach to enhancing immunogenicity in PDAC, the evidence supporting the main hypotheses needs to be significantly improved. The detailed points are listed below.\nMajor points\n1. The clinical data analysis in Figure 1 shows an association between GATA6 expression and the inflammatory response signature including MHCI up-regulation. However, it remains unclear how these findings relate to the therapeutic context of MEKi, since the patient dataset indicates these associations exist independent of MEK inhibition. The observation on clinical data is consistent with preclinical models? GATA6 positive cell lines tend to have the inflammatory response signature and MHCI up-regulation (without MEKi)? As another example, in GATA6high PDX vs. GATA6low PDX in figure 4, do they have different MHCI expression level?\n2. The induction of MHCI in GATA6+ pancreatic cancer cells upon MEKi treatment was shown in a couple of KPC cell lines. However, this does not appear robust enough or whether similar dynamics occur in human PDAC cell lines across different molecular subtypes (PDAC cell lines known to belong to classical subtype vs. basal-like).\n3. In figure 3, the analysis seems limited as it tests GATA6-dependent MHCI expression and T-cell cytotoxicity mainly in one cell line each. Expanding this to more diverse cell lines could strengthen the conclusions drawn. It is not clear whether differential expression of MHCI is functionally important to determine anti-tumor activity.\n4. The introduction of HDAC inhibitors is abrupt and their role in the combinatory treatment effect alongside MEKi is not sufficiently explained.\n5. When combo-treatment is done in PDX models (Figure 4), is the therapeutic effect mainly through MHCI-T cell mediated cytotoxicity? If so, PDX models in immune-compromised or -deficient hosts show no obvious difference since they lack adaptive immune system?\n6. Figure 5e data seems contradictory with what the authors showed so far. MEKi treatment did not up-regulate MHCI expression. HDACi treatment didn’t seem to retain GATA6 positive population either. Only combo context resulted in up-regulation of MHCI (no change in GATA6 positive population). This also raise a concern whether the main hypothesis was thoroughly tested and the conclusion is supported by robust data.\nMinor points\n1. In Figure 3a, the figure is not complete.\n2. In figure 4, the graphs do not have some information such as y-axis titles and units.\n3. In Figure 4, for the PDX experiments, the authors used mocetinostat as HDACi, and then switched to dometinostat. Any reason?\n4. Figure 5e & 5g, it is questionable whether it accurately counted GATA6+ and MHCI+ cells. The positive cell population ranges only 2-8% in the entire population in a given area?"}, "author_response": " We thank the reviewer for the thorough review and careful, well-stated concerns and suggestions. As described below, we have significantly extended our data and modified the manuscript to add depth and give more details.\nConcerns:\nWriting issues:\n1.) The introduction and some of the discussion are not well written and lack clarity for multiple statements; they are generally broad and do not provide the reader with accurate/specific interpretations from the cited references. Especially with respect to previous studies on GATA6 in the introduction. Additionally, the First two lines in the abstract are too broad, unsupported, and should be rewritten.\nResponse:\n Thank you for your comments. We have substantially revised the Introduction, Abstract, and relevant parts of the Discussion to improve clarity, specificity, and scientific accuracy. In particular, the first two lines of the abstract have been rewritten to avoid overly broad statements and better reflect the content and significance of the study.\n2.) Multiple small grammatical issues are detected throughout the manuscript.\nResponse:\n We have checked for grammatical mistakes thoroughly and revised accordingly in the revised version.\n3.) Methods for ultrasound imaging of the tumors is not provided.\nResponse:\n The methods for ultrasound imaging have been previously described in detail in our published study 1. Additionally, we have now included representative ultrasound images in Fig. 3c of the revised manuscript.\n1\n4.) GP99 cells should not be portrayed as GATA6- as this implies negative; in reality they do express low levels (fig3d). It is suggested to use the term GATA6lo. It is also suggested that a GATA6 knockout be test as cancer cells are highly plastic and the ability to reexpress GATA6 in lo cells is unclear.\nResponse:\n We thank the reviewer for the helpful suggestions. In the revised manuscript, the original Figure 3 has been reorganized and is now presented as Figure 5. In the revised manuscript, we have updated our terminology to describe GP99 and similar cell lines as GATA6 rather than GATA6⁻, to more accurately reflect their low but low detectable GATA6 expression, as shown in Fig. 5c (original Fig. 3d).\nTo directly address the reviewer’s concern regarding cancer cell plasticity and the potential for GATA6 re-expression, we generated GATA6 knockout lines using CRISPR-Cas9 in the GATA6high cell line 2838c3 (Fig. 2d–e, Fig. S2b). We derived two independent knockout clones (gGATA6_KO-3 and gGATA6_KO-4), and found that the MEKi-induced upregulation of MHCI seen in the parental line was largely abolished in the KO clones. These results demonstrate that GATA6 is required for the MEKi- mediated induction of MHCI in vitro.\nTo validate the role of GATA6 in vivo, we employed a targeted GATA6 protein degradation system using the orthotopic transplantation model of auxin-inducible degron GATA6 knock-in (AID-GATA6-KI) cells derived from 110299, another GATA6high cell line, rather than GATA6-KO cells (Fig. 3e-h, S3d-f). This approach was chosen because GATA6’s strong interaction with the immune microenvironment could potentially alter tumor formation and baseline tumor microenvironment (TME) in GATA6-KO models, complicating the assessment of drug-induced effects and leading to different conclusions. With the AID-GATA6-KI system, we were able to reduce GATA6 protein expression after tumor establishment, and GATA6 levels remained low throughout the treatment period upon auxin administration (Fig. 3g, S3d). We found that GATA6 degradation significantly impaired MEKi-induced tumor suppression in the 110299 orthotopic model (Fig. 3f). Additionally, the MEKi-induced upregulation of tumor MHCI expression, CD8 infiltration, and anti-tumor cytotoxicity were all markedly reduced (Fig. 3h), highlighting GATA6’s essential role in MEKi-induced anti-tumor responses. Notably, MEKi-induced CD4 infiltration was unaffected by GATA6 degradation (Fig. S3f), suggesting that GATA6 specifically mediates the MEKi effect via the MHCI-CD8 pathway.\n5.) GATA6 does not “orchestrate” EMT, instead the reference demonstrates that GATA6 is required to maintain epithelial differentiation. The following is therefore quite misleading and should be rewritten : “Recent seminal studies have illuminated the crucial tumor-suppressive role of GATA6 in orchestrating epithelial-to-mesenchymal transition (EMT) and restraining tumor dissemination in PDAC 4-6.”\nResponse:\n We have revised the text as “GATA6 is a key regulator of the classical PDAC phenotype, maintaining epithelial differentiation while restraining epithelial-to- mesenchymal transition (EMT)” in page 6 line 2-3.\n6.) The following statement is also without a value “Despite the prognostic and predictive value of GATA6 loss in response to adjuvant chemotherapy 6”, authors should clearly state if this is positive or negative.\nResponse:\n We have revised the text to provide a more specific statement “Retrospective studies have shown strong associations between high GATA6 expression and prolonged survival in treatment-naive and advanced PDAC cases” in page 6 line 5-6.\n7.) The following statement is also without a value “Histone deacetylase inhibitors (HDACi) are known to affect EMT-like features of cancer cells and tumor-stromal crosstalk in PDAC 18,19”, authors should clearly state if this is positive or negative.\nResponse:\n We have revised the text to provide a more confirmative statement as “Histone deacetylase inhibitors (HDACi) are known to promote epithelial gene expression in tumor cells and reduce pro-tumorigenic stromal activation in PDAC” in page 7 line 3- 4.\nExperimental issues:\n1.) Baseline proliferation rates should also be shown for each cell line and each treatment.\nResponse:\n We thank the reviewer for this important suggestion. In the revised manuscript, we performed a BrdU assay to assess the proliferation status of all cell lines in vitro (Fig. S2a). While we observed differences in baseline proliferation rates among the cell lines, no correlation was found with their baseline GATA6 expression levels. For MEKi- treated cells, there was a noticeable but statistically non-significant reduction in proliferation (Fig. S2a), and the extent of this decrease was not associated with GATA6 expression changes.\n2.) It is unclear if the MHCI expression correlates with active proliferation in the cells. As both GATA6 loss and MAPK activity are likely to associate with altered proliferation. The low MHCI expression could be a result cells in an active cell cycle, which would likely not have MHCI on the surface as they have freshly divided. Many of the results are correlative and may not indicate a direct regulation of MHCI.\nResponse:\n As mentioned above, we assessed the proliferation status of the cells both at baseline and after treatment. No significant correlation was observed between proliferation status and GATA6 expression, or between proliferation status and MHCI levels (Fig. S2a, Fig. 2a,2c). Additionally, we examined the co-expression of MHCI and GATA6 with the proliferative marker PCNA in our human PDAC specimens (n=8) and found no correlation between proliferation status and MHCI and/or GATA6 expression (Fig. S1d).\n3.) Figure 3: Experiments in figure 3f should be performed in the presence of anti-PD1 as a control to determine if this will eliminate the difference in between GP82 and GP99 experiments.\nResponse:\n We thank the reviewer for this thoughtful comment. In the revised manuscript, the original Figure 3 has been reorganized and is now presented as Figure 5.\nAlthough we observed a significant increase in PD1+ cells following combination treatment in vitro (Fig. 5e,f) and in vivo (Fig. 7a), we did not include anti-PD1 therapy in these experiments. This was because cytotoxic activity remained robust, as evidenced by high GzmB⁺ cell frequencies—and tumor growth was already well controlled, indicating that additional PD1 blockade might not further enhance anti-tumor effects.\nIn fact, the limiting factor for the combination treatment in vivo was not tumor progression but treatment-related toxicity, as evidenced by significant weight loss (Fig. S7a), which led to early termination of the experiment despite minimal tumor burden.\nImportantly, the central aim of our study is to define the immunological role of GATA6 expression in shaping therapeutic responses, particularly through its influence on tumor cell state plasticity. The MEKi/HDACi combination serves as a proof-of-concept strategy to preserve the GATA6⁺ tumor population and enhance immune responsiveness, thereby supporting our broader hypothesis that GATA6-mediated tumor cell states are key determinants of treatment efficacy.\n4.) Figure 5: Figure 5a, body weights (stated in results section) are missing from the figure. Figure 5b,c it is unclear why the mice died if the tumors were decreasing in volume. This should be explored and explained. Figure 5f, the MHCI IHC is very faint if any protein in detectable at all from the image provided and is not very convincing or representative of the quantitative data in 5g. The cellular localization of GATA6 is not well explained and appears to shift from cytoplasmic to nuclear based on the treatment provided. Further fractionation analysis should be provided to quantify.\nResponse:\n We appreciate the reviewer’s detailed observations. The original Figure 5 has now been reorganized and is presented as Figure 6 in the revised manuscript.\n Regarding the body weight data, we have now included the data in the revised supplementary figure (Fig. S7a). In the in vivo experiment with combination treatment, mice were sacrificed due to significant weight loss, despite small tumor burdens. As the data indicate, MEKi alone didn’t lead to significant body weight loss, whereas the combination of MEKi and HDACi induced substantial weight loss. These findings are consistent with previous reports of HDACi-associated adverse effects, such as gastrointestinal toxicity and constitutional symptoms, which have been observed in clinical trials 2. While HDACi toxicity limited treatment tolerability, its use in our study was primarily as a mechanistic tool to restore and maintain the GATA6⁺ tumor cell population, thereby enabling us to test the hypothesis that preserving this cell state can enhance therapeutic responses.\n Regarding MHCI staining, to better visualize the increase in MHCI expression on GATA6⁺ tumor cells upon MEKi and HDACi treatment, we performed mIF on all four treatment groups, allowing comparison of MHCI expression and its co-expression with GATA6. High-resolution mIF images are included in the revised manuscript (Fig. 6f), and the quantification corresponds well with the observed staining patterns.\n Regarding the GATA6 localization, since GATA6 is a transcription factor, its signal should be localized mainly to the nucleus. To address cytoplasmic staining observed in the original manuscript (Fig. 5e), we repeated the staining using an optimized\n4\nprotocol in the revised manuscript (Fig. 6e), which yielded nuclear-specific GATA6 signals. All quantifications were based exclusively on nuclear GATA6 staining. Given that the cytoplasmic signal was artifactual and has now been corrected with specific nuclear staining, we believe additional subcellular fractionation is not required at this point.\n5.) Rationale for using the HDACi is limited and not rigorously justified. With the broad spectrum impact of HDAC inhibitors it is unclear how this study will benefit the field. The further lack of consideration for off target effects of the HDAC inhibitors is also concerning; these could have indirect effects that obscure the proposed mechanism.\nResponse:\n We thank the reviewer for this important comment. We agree that HDAC inhibitors have broad epigenetic effects and that mechanistic clarity is critical when interpreting their impact. In our study, HDAC inhibition was employed as a proof-of-concept tool to restore the GATA6+ tumor cell population that is responsive to MEKi treatment. This approach demonstrates that modulating tumor cell states can enhance therapeutic outcomes.\nWe selected HDACi based on prior studies showing that HDAC activity promotes EMT, and that HDAC inhibition can reinforce epithelial lineage programs in PDAC, including GATA6 expression (e.g., through BRD4- and MYC-dependent mechanisms as described for Domatinostat) 3. Since MEKi treatment consistently reduced GATA6 expression and promoted EMT features, we hypothesized that HDAC inhibition could stabilize GATA6 levels and thereby preserve a MEKi-responsive tumor cell population. Indeed, we demonstrated that HDACi treatment largely restores GATA6 expression in vitro and in vivo, significantly improving tumor control and mouse survival. The revised manuscript includes data confirming GATA6 restoration by HDACi both in vitro (Fig 4d- e) and in vivo (Fig. 6e-f, S5d).\nTo address potential off-target or indirect immune effects of HDACi, we compared the immune-related transcriptomic profiles of CKP tumors treated with or without MEKi and HDACi using the NanoString PanCancer Immune Profiling Panel. Gene set variation analysis (GSVA) showed that MEKi monotherapy induced several immune- related gene sets including antigen receptor signaling, T cell activation, cytotoxicity, and type II interferon signaling (Fig. 6d). In contrast, HDACi alone had a modest impact, enriching for antigen processing and IFN-γ-related pathways. Notably, the combination therapy activated a broader range of immune pathways than either monotherapy, including additive or synergistic enrichment of the pathways noted above (Fig. 6d).\nFurther differential expression analysis between MEKi and combination-treated tumors revealed that the combination therapy significantly upregulated genes associated with T cell function and antigen presentation (Fig. S7b,c). Additionally, the combination group exhibited higher scores in pathways related to antigenicity, such as MHC and antigen processing (Fig. S7b, c), suggesting that combined MEKi and HDACi enhances tumor antigenicity compared to MEKi alone. Taken together, these data support our use of HDACi as a mechanistic tool to modulate tumor cell state, restore GATA6 expression, and improve immunogenic responses to MEKi. We have clarified this rationale and expanded the description of the transcriptomic data in the Results section of the revised manuscript.\nIn addition, the toxicity profile of HDAC inhibitors is a critical limitation for their broader clinical use and is discussed in more detail in the revised Discussion section.\nConsistent with clinical observations, in our in vivo experiments the combination of\n6.) Authors fail to describe or differentiate how HDAC and MEK inhibitors directly affect T cells or other stromal cells and the implications that could have for these studies.\nResponse:\n We agree with the reviewer and have worked intensively on better characterizing the role of both targeting approaches. In order to delineate the effects induced by HDACi and MEKi separately, we analyzed the immune transcriptomic profiles of CKP tumors treated by the single treatment of the two inhibitors (Fig. 6d). Gene set variation analysis revealed that MEKi alone induced the expression of several immune-related gene sets, including antigen receptor-mediated signaling, T cell receptor signaling, type II interferon activation, T cell activation, and cytotoxicity (Fig. 6d), suggesting its role in promoting T cell-mediated immunity. In contrast, HDACi alone had a more subtle effect on the tumor immune microenvironment, inducing pathways related to antigen processing and type II interferon regulation (Fig. 6d).\nWe have included the above data in the revised manuscript (Fig. 6d).\n7.) Lack of validation/controls for GATA6 or MEK regulation in response to HDAC inhibitors. Authors should monitor these in response to HDAC inhibition. nuclear versus cytoplasmic ratio of GATA6 in response to each treatment.\nResponse:\nWe thank the reviewer for highlighting the need to validate GATA6 changes upon HDAC inhibition. To address this, we evaluated GATA6 expression changes in response to HDAC inhibitors both in vitro (Fig. 4d-e) and in vivo (Fig. 6e-f). Our data show that HDAC inhibition restores GATA6 expression without causing significant fluctuations in overall levels.\nRegarding GATA6 expression, since GATA6 is a transcription factor, its signal should be predominantly nuclear. In the original manuscript (Fig. 5e), some cytoplasmic staining was observed; to clarify this, we optimized the immunostaining protocol for the revised manuscript, which now yields specific nuclear GATA6 signals (Fig. 6e). All quantifications were performed exclusively on nuclear GATA6 staining. Since the previous cytoplasmic signal was artifactual and corrected by the improved protocol, we believe that additional nuclear vs cytoplasmic quantification is not necessary.\nReviewer #2 (Remarks to the Author): with expertise in pancreatic cancer\nYang and co-authors present new data on the role of GATA6 in regulating immune responsiveness and MHC-I expression in the mouse and human models of PDAC. The study is well-designed and presents compelling evidence derived from genetically engineered cell lines and animal models. The main thrust of the paper is to demonstrate that MEKi increase MHC-I expression and type-I IFN signatures selectively in GATA6 classical subsets of PDAC, whereas GATA6- low EMT PDAC cells evade immune surveillance.\nResponse:\n We sincerely thank the reviewer for the positive evaluation of our study and for recognizing the significance of our findings.\nSeveral issues would be important to address to solidify the overall impact:\n1) The proposed immunological mechanism of MEKi+HDACi is not sufficiently validated. Experiments to reverse the survival advantage by depletion of CD8/CD4 cells or in immunodeficient hosts will be important to exclude non- immune mechanisms of MEKi+HDACi combinatorial activity.\nResponse:\n We thank the reviewer for this important suggestion and agree that further validation of the immunological mechanisms underlying MEKi and HDACi combination therapy is essential. Accordingly, we have expanded our experimental efforts to clarify the immune contributions in greater detail.\nFirst, we utilized the AID-GATA6-KI system to inducibly reduce GATA6 protein expression after tumor establishment. Upon auxin administration, GATA6 levels remained low throughout treatment (Fig. 3g, S3d). We observed that GATA6 degradation significantly impaired MEKi-induced tumor suppression in the 110299AID- GATA6 orthotopic model (Fig. 3f). Furthermore, the MEKi-driven upregulation of tumor MHCI, CD8+ T cell infiltration, and anti-tumor cytotoxicity were markedly diminished (Fig. 3h), highlighting GATA6’s essential role in MEKi-induced anti-tumor responses. Notably, MEKi-induced CD4 infiltration was unaffected by GATA6 degradation (Fig. S3f), suggesting that GATA6 specifically mediates the MEKi effect via the MHCI-CD8 pathway.\nSecond, we evaluated the combined MEKi and HDACi treatment in both the 110299AID-GATA6 orthotopic model (Fig. S6a,c,d) and CKP tumor-bearing mice (Fig. 6). Compared to MEKi monotherapy, which reduced GATA6 expression but increased MHCI levels, the combination treatment restored GATA6 expression while maintaining high levels of MHCI (Fig. 6e-f, S6a,d) and resulted in stronger tumor control (Fig. 6b- c, S6c).\nIn order to confirm the role of CD8, we performed additional experiment to deplete CD8 cells with anti-CD8 antibody (Fig. S6b-d), and here we observed that CD8 depletion greatly abolished the tumor-suppressive effect of the combined treatment.\nHistological analysis revealed that tumor cell GATA6 expression remained largely unchanged, while MHCI expression stayed elevated following MEKi and HDACi combination therapy, regardless of CD8 status, suggesting that MHCI upregulation is independent of CD8+ T cell activity (Fig. S6d). In contrast, the increase of Cl casp3 after MEKi and HDACi combination therapy was abolished when CD8+ T cells were depleted, further supporting the critical role of CD8+ T cells in mediating the anti-tumor effects of the combined MEK and HDAC inhibition.\nThese new data have been incorporated into the revised manuscript (Fig.3; Fig.S3e- f; Fig. 6b-f; Fig. S6).\n2) The ex vivo experiments with T cell and PDAC co-cultures should be feasible to model in vivo in the TCR Tg mice. This or other immunogenic models (e.g., Ova/OT-I TCR Tg) cold be used to demonstrate the efficacy of MEKi+HDACi combination.\nResponse:\n To demonstrate the efficacy of MEKi and HDACi combination in vitro, we have included the combination treatment in the co-culture experiments of the gp33- expressing tumor cells with the gp33-TCR transgenic T cells. Besides, in the revised manuscript, we included 2 additional gp-expressing murine PDAC cell lines. The results showed that combined MEKi and HDACi treatment induces MHCI upregulation on PDAC cells, which was translated into enhanced tumor-specific T cell cytotoxicity.\nWe have included the results in the revised manuscript in Fig. 5.\n3) By the same token, the cellular model of GATA6 downregulation (or alternatively using Dox-shGATA6) should be deployed in vivo to valiadate the effect on tumor growth and MHC-I.\nResponse:\n To validate the role of GATA6 in vivo, we employed a targeted GATA6 protein degradation system using the orthotopic transplantation model of auxin-inducible degron GATA6 knock-in (AID-GATA6-KI) cells derived from 110299, another GATA6high cell line, instead of complete GATA6 knockout cells (Fig. 3e-h, S3d-f). We chose this approach because the strong interaction of GATA6 with the immune microenvironment could profoundly alter tumor initiation and baseline tumor microenvironment in GATA6 knockout models, complicating the interpretation of drug-induced effects and potentially leading to confounding conclusions.\nWith the AID-GATA6-KI system, we achieved inducible reduction of GATA6 protein expression after tumor establishment, with sustained low GATA6 levels throughout the treatment period upon auxin administration (Fig. 3g, S3d). We observed that GATA6 degradation significantly impaired MEKi-induced tumor suppression in the 110299 orthotopic model (Fig. 3f). Additionally, the MEKi-induced upregulation of tumor MHCI expression, CD8 infiltration, and anti-tumor cytotoxicity were all markedly reduced (Fig. 3h), highlighting GATA6’s essential role in MEKi-induced anti-tumor responses.\nNotably, MEKi-induced CD4 infiltration was unaffected by GATA6 degradation (Fig. S3f), indicating that GATA6 specifically influences the MEKi effect via the MHCI-CD8+ T cell axis.\nMinor comments:\n1) please describe how Gata6 hi and lo cell were separated by FACS (Fig.2a).\nResponse:\n Cells were fixed, permeabilized, and stained with a GATA6-specific antibody or the corresponding isotype control. Flow cytometric analysis was then performed to measure GATA6 levels. Based on the median fluorescence intensity (MFI) of GATA6 staining, cells were classified into GATA6high and GATA6 populations, as shown in low Fig. 2a.\n2) Figure 3c and 3f are poorly visible. Suggest higher magnification. Controls of MEKi alone should be provided to demonstrate its direct effect on viability. Figure legend does not explain the color in the image- is it CFSE in T cells? Are PI images available or was it done by FACS?\nResponse:\n In the revised manuscript, the original Figure 3 has been reorganized and is now presented as Figure 5. We have replaced the relevant images with higher-resolution versions to improve visibility (Fig. 5e, f), as suggested.\nTo address the reviewer’s concern regarding MEKi controls, we have now included cytotoxicity data from gp-expressing tumor cells treated with MEKi and/or HDACi in the presence (Fig. 5e, f) or absence of gp-TCR T cells (Fig. S5h). These results demonstrate that in the absence of T cells, the treatment does not induce statistically significant cytotoxicity, indicating that the observed effects are largely T cell–mediated rather than a direct consequence of MEKi or HDACi treatment alone.\n We have also revised the figure legend to clarify the color coding: the green signal corresponds to CFSE-labeled T cells.\n Regarding PI staining, detection of PI+ tumor cells was performed by flow cytometric analysis, and this information has been added to the corresponding figure legend (page 61, line 19-20) for clarity.\n3) Figures 6 and 7 provide indirect correlations without direct mechanistic evidence. These can be enhanced and replaced with direct results demonstrating the role of T cell immunity in the context of genetic or pharmacological GATA6 modulation.\nResponse:\n To provide more direct mechanistic evidence of the role of T cell immunity in the context of GATA6 modulation, we expanded our experimental approaches in the revised manuscript.\nFirst, we included the AID-GATA6-KI system, which was able to reduce GATA6 protein expression after tumor establishment, and GATA6 levels remained low throughout the treatment period upon auxin administration (Fig. 3g, S3d). We found that GATA6 degradation significantly impaired MEKi-induced tumor suppression in the 110299AID- GATA6 orthotopic model (Fig. 3f). Additionally, the MEKi-induced upregulation of tumor MHCI expression, CD8 infiltration, and anti-tumor cytotoxicity were all markedly reduced (Fig. 3h), highlighting GATA6’s essential role in MEKi-induced anti-tumor responses. Notably, MEKi-induced CD4 infiltration was unaffected by GATA6 degradation (Fig. S3f), suggesting that GATA6 specifically mediates the MEKi effect via the MHCI-CD8 pathway.\nSecond, we evaluated the combined MEKi and HDACi treatment in both the 110299 orthotopic model (Fig. S6a,c,d) and CKP tumor-bearing mice (Fig. 6). Compared to MEKi monotherapy (Fig. S4a; Fig. 3d), which reduced GATA6 expression (Fig. S4a) but increased MHC-I levels (Fig. 3d), the combination treatment restored GATA6 expression while maintaining high levels of MHCI (Fig. 6e-f, S6d) and resulted in stronger tumor control (Fig. 6b-c, S6c).\nIn order to confirm the role of CD8, we performed additional experiment to deplete CD8 cells with anti-CD8 antibody (Fig. S6b-d), and here we observed that CD8 depletion greatly abolished the tumor-suppressive effect of the combined treatment.\nHistological analysis revealed that tumor cell GATA6 expression remained largely unchanged, while MHCI expression stayed elevated following MEKi and HDACi combination therapy, regardless of CD8 status, suggesting that MHCI upregulation is independent of CD8+ T cell activity (Fig. S6d). In contrast, increase of Cl casp3 after MEKi and HDACi combination therapy was abolished when CD8+ T cells were depleted, further supporting the critical role of CD8+ T cells in mediating the anti-tumor effects of the combined MEK and HDAC inhibition.\nThese data provide direct mechanistic evidence that GATA6 modulates anti-tumor immunity primarily via MHCI-mediated CD8+ T cell responses, and have been incorporated into the revised manuscript (Fig. 3; Fig. S3e-f; Fig. 6b-f; Fig. S6).\nReviewer #3 (Remarks to the Author): with expertise in pancreatic cancer\nYang et al. present evidence that GATA6+ expression in PDAC tumors and cell lines is required for MEK inhibition to promote tumor immunity via increased expression of MHC-I and IFN-gamma. However, long-term MEK inhibition in vivo is ineffective due to lower GATA6 expression and consequently, less antigen presentation. The authors show that HDAC inhibition can prevent GATA6 downregulation in tumors, and thus work synergistically with MEK inhibition. GATA6 expression may serve as a biomarker of responsiveness to MEK+HDAC inhibitor combinations. These findings are intriguing and may have clinical relevance; however, several of the principal findings would be more convincing if supported by more in-depth characterization and by using additional cell lines.\nResponse:\n We sincerely thank the reviewer for the thoughtful summary and constructive comments. We appreciate the recognition of the potential clinical relevance of our findings, particularly regarding the role of GATA6 in modulating tumor immune responses to MEK and HDAC inhibition. We fully agree that further in-depth characterization and validation across additional models would strengthen the conclusions, and we have taken steps to address these points in the revised manuscript.\nLarger cohort and alternate analyses needed for data from patient tumors.\nThe authors show that GATA6-high patient tumors harbor denser populations of immune cells (both anti-tumor CD8+ T cells and pro-tumor FOXP3+ T cells) compared to GATA6-low tumors. In PDAC, there is a negative correlation between tumor cell density and the densities of many immune cell types (both anti-tumor and pro-tumor). The authors should report whether tumor cellularity per se aligns with leukocyte density. Is there a relationship between tumor cellularity within groups (GATA6-high, GATA6-int, and GATA-6-low)?\nResponse:\n We thank the reviewer for this insightful point and fully agree on the need to clarify the relationship between GATA6 expression, tumor cellularity, and immune cell infiltration. To address this, we analyzed the correlation between the percentage of CK19+ area per total tissue core area (as for tumor cellularity), GATA6 expression levels, and the densities of various immune cell types. Our analysis revealed a weak but statistically significant positive correlation between GATA6 expression and tumor cellularity. However, none of the immune cell markers showed a negative correlation with tumor cellularity (see Fig. 1 and Table 1 for reviewers). Notably, CD20 and CD68 densities in the stroma were even positively correlated with high tumor cellularity (Fig. 1 and Table 1 for reviewers). It is important to note that for all stromal-restricted immune markers, cell densities were normalized to the stromal area, ensuring that observed differences in immune cell abundance are not confounded by variations in tumor cellularity.\nThe GATA6 staining from CODEX in Figure 1D and E appears regional, and may be due to low-quality tissue in that region. Although the PanKRT stain is high throughout, it’s difficult to tell how thresholds were set to detect staining differences. A counter stain for basal-like tumor cells in Supp Figure 1 appears to show no KRT5 staining, consistent with regional tissue quality problems. If KRT5 is not expressed by this tumor, KRT6A, and TP63 are other options. Additionally, a single patient specimen is insufficient for drawing conclusions about intratumoral GATA6 heterogeneity and leukocyte proximity. This data should be supported by simple immunofluorescence co-staining for GATA6, a basal-like marker, and 1 or 2 leukocyte markers in multiple ROIs from ≥5 patient tumors. Tumors with relatively uniform classical-subtype tumor cells and tumors with predominantly basal-like tumor cells should be added as controls.\nResponse:\n We fully agree that appropriate thresholds, biological controls, and sufficient sample size are critical to support our conclusions.\nTo address these concerns, we have now included data from 7 additional patient tumors subjected to multiplex immunofluorescence and spatial analysis. This includes two resected tumors, each with four representative ROIs, and six additional tumors from a tissue microarray (TMA), in which full core sections were analyzed. For each case, average values from the multiple ROIs or TMA cores are shown.\nClinicopathological characteristics of all patients are now provided in Supplementary Table 5. The inter-patient heterogeneity of GATA6 expression is evident in Fig. S1e, supporting the biological variability of this marker in human PDAC.\nTo assess immune cell distribution and proximity to tumor subtypes, we analyzed 10 markers, including GATA6, KRT5, and key immune markers such as CD4, CD8, CD20, and CD68. This allowed us to examine spatial relationships between tumor cells and leukocyte populations across multiple tumor contexts. These additional data are presented in revised Fig. 1 and Supplementary Fig. S1.\nRegarding the concern about possible tissue quality artifacts in the original sample, we quantified KRT5⁺ cells across all eight tumor samples (Fig. S1e). We found that three tumors exhibited positive KRT5 expression, while the remaining did not, indicating that the absence of KRT5 in the original index case reflects tumor-intrinsic biology rather than technical failure. We appreciate the reviewer’s suggestion and have added this quantification to the revised manuscript.\nThese new data, presented in Fig. 1 and Supplementary Fig. S1, provide a more comprehensive foundation for the conclusions drawn in the manuscript.\nThe authors suggest that leukocyte density is related to GATA6 expression and not CLDN18 expression (also considered a classical subtype marker), but the comparison between CLDN18+ and CLDN18neg tumor cells in Supp Figure 1 and their proximity to immune cells is less informative than a comparison between CLDN18+GATA6neg and CLDN18negGATA6+ tumor cells. Statistics are also missing for this data.\nResponse:\n In the revised manuscript, we have reanalyzed the data by comparing between GATA6+CLDN18- and GATA6-CLDN18+ tumor cell populations (Fig. S1e, f). This refined analysis revealed that GATA6⁺CLDN18⁻ tumor cells exhibit significantly stronger spatial interactions with CD8⁺ T cells, whereas GATA6⁻CLDN18⁺ cells do not show such enrichment (Fig. S1f). A recent publication highlighted the role of tumor- derived CLDN18 in promoting T lymphocyte infiltration and antitumor immunity in PDAC 4.\nOur analysis shows a high degree of co-expression between GATA6 and CLDN18 in PDAC tumor cells (Fig. S1e). This co-expression may have confounded previous interpretations attributing immune cell proximity solely to CLDN18. The specific enrichment of CD8⁺ T cells near GATA6⁺CLDN18⁻ cells (Fig. S1f) suggests that GATA6, rather than CLDN18, may play a dominant role in orchestrating tumor–T cell interactions. Statistical comparisons have been added to Fig. S1f to support these conclusions.\nThe relationship between GATA6 expression and the classical subtype is important context, but is not sufficiently developed, especially with the patient tumor analyses. One of the many published subtyping approaches should be applied to patient tumor gene expression data, as well as to individual cells from scRNAseq datasets and LCM tumor regions. Additionally, there are several published scRNA-seq datasets that should be used to identify tumor cell-specific GATA6 expression (like the dataset used for Figure 1C). The Mauer et al. LCMD data is less ideal than scRNAseq datasets for addressing cell-level heterogeneity.\nResponse:\n We appreciate the reviewer’s thoughtful suggestion. In this study, we focus on the epithelial features of GATA6+ tumor cells rather than defining GATA6 as a classical subtype marker. While many studies support GATA6 as a marker of the classical PDAC subtype, accumulating evidence suggests substantial plasticity within PDAC subtypes, including the potential for tumor cells to transition between classical- and basal-like programs. This dynamic nature complicates rigid subtype assignments and supports a more nuanced view of tumor cell states.\nIn line with this, our spatial analysis of human tumors (Fig. S1e) shows that GATA6 and the classical marker CLDN18 are frequently, but not universally, co-expressed, indicating heterogeneity even within putative classical tumors. Thus, rather than applying a predefined subtyping framework, we focus on GATA6⁺ tumor cells as a biologically relevant subpopulation with epithelial features and immune relevance.\nWe agree that scRNAseq datasets are highly informative for characterizing tumor heterogeneity. In our study, we already analyzed a published scRNA-seq dataset (Fig. 1c), which revealed a strong inverse relationship between GATA6 expression and EMT-associated programs. This supports the interpretation that GATA6 marks an epithelial-like tumor cell state.\nWe propose that GATA6 may reflect a dynamic transcriptional state rather than a fixed subtype identity, particularly given its variable expression levels across tumor cells. As a transcription factor with pleiotropic roles including in immunomodulation, we chose to focus on the functional implications of its expression pattern, especially in shaping the tumor–immune microenvironment, rather than fitting it into a rigid subtype classification. We hope this clarifies our rationale and approach.\nMore mouse cell lines needed for in vitro mechanism experiments\nResponse:\n We agree with the reviewer, and in the revised manuscript, we have substantially increased the number of murine cell lines for in vitro experiments (Fig. 2,4). Besides, in addition to more CKP, KPC or KPCY derived cell lines, we included cell line models with GATA6 knock out and auxin-inducible degron (AID) GATA6 knock in (Fig. 2; Fig. S2) and additional 2 more cell lines with GP expression (Fig. 5).\nThe authors generated cell lines from syngeneic KPC mice. Since these lines were not subcloned, it is surprising that 3 of them show heterogeneous GATA6 expression, and 3 show no GATA6 expression at all (Figure 2A). The authors should genetically validate that these lines are exclusively tumor cells, and these experiments should be done with FACS-sorted GATA6-high and GATA6-low cells from several heterogeneous cell lines. The top histogram in Figure 2A shows an unusual bi-modal expression of GATA6 with many cells on the right y-axis. These data should be inspected for FACS artifacts, and a better example plot should be presented.\nResponse:\n We thank the reviewer for this valuable comment and apologize for the lack of clarity in the original manuscript. The murine PDAC cell lines used in our study were derived from primary tumors of genetically engineered CKP, KPC, or KPCY mice, and each cell line was established from an independent tumor. While the lines were not subcloned, they were expanded from early-passage tumor cell outgrowths and have been validated to exhibit stable phenotypes.\nWe assessed GATA6 expression across 9 cell lines by flow cytometry and categorized them into GATA6high (n=5) and GATA6 (n=4) based on the median value of the mean low fluorescence intensity. Importantly, each line exhibits a unimodal distribution of GATA6 signal, consistent with homogeneous expression within each culture. The previously noted “bi-modal” appearance in Figure 2a was due to the overlay of the GATA6 signal with the isotype control, rather than true subpopulations of differing GATA6 expression. We apologize for the lack of clarity in the original figure. To address this, we have revised Figure 2a to clearly label the isotype control and GATA6 antibody (GATA6 AB)- stained populations, which we believe will eliminate this confusion.\nAdditionally, it appears that only 4 of the 6 cell lines are used for the data in Figure 2c (it’s unclear how many were used for Figure 2b). How were these selected?\nResponse:\n In Fig. 2b, we used 6 cell lines (three GATA6high: 511950, 60400, 70301 vs three GATA6 : 60531, 511892, 60590) for transcriptomic analysis. We acknowledge the low limited representation in Fig. 2c, and in the revised manuscript, we have expanded the in vitro experiments to include all these 6 cell lines, as well as 2 additional cell lines to strengthen the conclusions (Fig. 2, Fig. 4). We have clarified this in the revised figure legends and Methods section.\nThe AID approach in Figure 2e-g more convincingly demonstrates the relationship between MEKi, GATA6, and antigen presentation, but should be performed on ≥3 distinct AID-altered lines.\nResponse:\n Thank you for this constructive suggestion. To strengthen the mechanistic link between GATA6 and MEKi-induced anti-tumor responses, we performed additional in vivo experiments using the 110299AID-GATA6 cells (Fig. 3e-h; Fig. S3d-f). This inducible model allowed us to dynamically modulate GATA6 expression within the tumor microenvironment and evaluate its role in MEKi responsiveness.\nBesides, in order to confirm the role of GATA6 in MEKi-induced MHCI expression, we generated two CRISPR-Cas9 GATA6 knockout clones from the GATA6high cell line 2838c3. In both clones, MEKi failed to induce MHCI expression, confirming the essential role of GATA6 in this pathway (Fig. 2d-e; Fig. S2b). Together, these complementary models support the functional importance of GATA6 in mediating MEKi-induced tumor immunogenicity.\nAdditionally, more than 2 cell lines should be derived from the FKPC2GP mice to make conclusions that the distinct morphologies from the 2 cell lines match mechanisms related to GATA6 and MEK inhibition. Similarly, the data in Figure 4e appear to be from 2 KPC cell lines, this in vitro experiment should be performed on ≥5 cell lines with statistical comparisons shown.\nResponse:\n In the revised manuscript, we have included 2 more GP-expressing cell lines for the co-culture experiment (Fig. 5). Additionally, for the in vitro combination treatment of MEKi and HDACi, we have substantially increased the number of murine PDAC cell lines to 8 (4 GATA6high vs 4 GATA6 ) (Fig. 4).\nlow\nThe treatment regimen for the in vivo experiment in Supp Figure 3b is not clear. Did the recurrence occur during MEKi treatment or did MEKi withdrawal prompt the recurrence? Additionally, the authors should provide MR images from the whole cohort data.\nResponse:\n We apologize for the unclear data presentation. After revision, the original Supplementary Figure 3 has now been reorganized and is now presented as the current Supplementary Figure 4 in the revised manuscript.\nTo address the reviewer’s questions, we have now included a detailed treatment schedule and timeline in the revised figure (Fig. S4b). Importantly, MEKi treatment was administered continuously and not withdrawn; tumor recurrence occurred during ongoing MEKi therapy. Additionally, we have expanded the imaging data to improve transparency. The revised supplementary figure now includes MR images across the entire treatment period for the cohort (Fig. S4c). In addition to MR images (Fig. S4c), we have also included the images from ultrasound in Fig. 3c in order to illustrate the tumor volume changes over time. We hope these additions clarify the treatment regimen and imaging data.\nThe Figure 5e figure legend states that the MHC-I and GATA6 staining is from tumor cells but also from “whole tumorous tissue”. This should be clarified. Figure 5g shows the percentage of tumor cells expressing GATA6 and MHC-I (though the y-axis is labelled only GATA6+) but it’s unclear if the bars are superimposed or stacked and statistical comparisons are missing. Figure 6 shows that with the treatment effect (greatest in the MEKi+HDACi combination) there is an increase in the percentage of leukocytes. Is this the percentage among all cells? As with earlier figures, these data would be easier to interpret with cell densities.\nResponse:\n We apologize for the lack of clarity in the original figures and legends. After revision, the original Fig. 5 and Fig. 6 have now been reorganized and are now presented as the current Fig. 6 and Fig. 7 in the revised manuscript.\nRegarding the term “tumorous tissue”, this refers to tumor regions delineated based on H&E and PanCK staining of the same tissue section, allowing us to exclude normal pancreatic areas from quantification. This quantification approach was previously described in our publication in Nature Communications 5 and utilizes both Definiens and HALO software.\nFor Fig. 5g (Fig. 6f in the revised manuscript), we have replotted the graph to show the individual values of GATA6+, GATA6+MHCI+, and GATA6+MHCI- tumor cells, making the categories visually distinct. Statistical comparisons have now been included, and significance is indicated where applicable.\nIn the revised Fig. 7 (the original Fig. 6), all percentages refer to the proportion of marker-positive cells relative to the total number of nucleated cells within the tumor regions. We have clarified this in the figure legends to aid interpretation.\nWe hope these revisions clarify our quantification approach and improve data interpretation.\nAdditional Comments\nFigure 1 and throughout, text on figure labels (especially axes labels) should be written in common syntax, without underscores and periods, and with appropriate superscripts.\nResponse:\n We have revised the figure labels accordingly in the revised manuscript.\nIn Figure 1 (and throughout) for GSEA results, the authors should indicate which pathways are considered significant – the color and size scales for NES and FDR are difficult to interpret; nominal P values should also be given.\nResponse:\n We have revised the figure labels accordingly in the revised manuscript. The pathways shown in the GSEA figures were all selected based on statistical significance (adjusted P-value < 0.05), as stated in the corresponding section of the text.\nNumbers are missing from the y-axes of Figure 4b and Figure 4c making it difficult to compare data between the charts. The values for GATA6 expression are similar between the control conditions of GATA6-hi and GATA6-low tumors for Figure 4c. Did GATA6 expression change in vivo or are the y-axes different?\nResponse:\n We apologize for the missing y-axis in Figure 4b and 4c, which may have been caused during image compression. We have included the y-axes of the graphs accordingly. Regarding the changes of GATA6 expression of the two in vivo models, the baseline levels and magnitude of changes are indeed different between the GATA6high and GATA6 tumors, in which GATA6high tumors show higher baseline of low GATA6 expression, and there is a significant reduction of GATA6 expression upon MEKi treatment. On the contrary, GATA6 expression of GATA6 tumors is low and low there is no prominent change in GATA6 level upon MEKi.\nReviewer #4 (Remarks to the Author): with expertise in pancreatic cancer\nYang et al. investigated the impact of combined MEK inhibitor (MEKi) and histone deacetylase inhibitor (HDACi) treatment on GATA6-dependent MHCI expression in pancreatic ductal adenocarcinoma (PDAC). The authors showed that MEKi alone increased MHCI expression in GATA6-positive pancreatic cancer cells and triggers epithelial-to-mesenchymal transition that appeared to reduce the effect. The addition of HDACi (domatinostat), preserved the GATA6-positive population and enhanced MHCI expression, resulting in improved immune response and decreased tumor growth. This combination therapy seemed to significantly increase the cytotoxic T-cell infiltration, enhancing anti-tumor responses. However, there appears to be a lack of coherence and consistency in linking the mechanistic insights across clinical data and experimental data. More importantly, it is not clear that MHCI up-regulation is mechanistically important for the anti-tumor immunity upon the MEKi/HDACi combo treatment, while MEKi/HDACi combo treatment might have pleiotropic effects on both tumor and stroma. While the study presents a novel approach to enhancing immunogenicity in PDAC, the evidence supporting the main hypotheses needs to be significantly improved. The detailed points are listed below.\nMajor points\n1. The clinical data analysis in Figure 1 shows an association between GATA6 expression and the inflammatory response signature including MHCI up-regulation. However, it remains unclear how these findings relate to the therapeutic context of MEKi, since the patient dataset indicates these associations exist independent of MEK inhibition. The observation on clinical data is consistent with preclinical models? GATA6 positive cell lines tend to have the inflammatory response signature and MHCI up-regulation (without MEKi)? As another example, in GATA6high PDX vs. GATA6low PDX in figure 4, do they have different MHCI expression level?\nResponse:\n We thank the reviewer for the important and insightful comments. The clinical data analysis in Fig. 1 highlights the immunological features of GATA6high tumors and GATA6+ tumor cells, emphasizing their enhanced ability to interact with immune cells, particularly CD8+ T cells. These features exist independently of therapy, indicating that GATA6 expression is associated with a more immunogenic basal state.\nIn our preclinical models, we observed that GATA6high tumor cells model (110299) tend to exhibit higher MHCI expression even without treatment (Fig. 3d) compared to GATA6 tumor cells model (60590, Fig. S3c), supporting the clinical observation. In low murine PDAC cell lines, we also found GATA6high lines tend to express higher levels of MHCI at baseline (Fig. 4f). These findings support a conserved relationship between GATA6 expression and immunogenicity, consistent across human and mouse datasets (Fig. 4f). In the PDX models (Fig. 4), we did not observe a clear difference in MHCI expression at baseline between GATA6high and GATA6 tumors. However, low GATA6high tumors consistently displayed greater MHCI induction upon MEKi treatment.\nImportantly, our study focuses on the therapy-induced dynamics of these features. Specifically, while GATA6+ tumor cells respond to MEKi by upregulating MHCI (as observed in the PDX model as well as CKP model, Fig. 4 and Fig. 6), GATA6 expression itself decreases following MEKi treatment (Fig. 4c, Fig. 6e, f), likely due to EMT induction. This therapy-induced mesenchymal shift limits sustained MHCI expression, thereby constraining the immunogenic potential of the tumor. This dynamic regulation of GATA6 cannot be easily dissected in patient samples and requires controlled experimental model systems for detailed investigation.\nTogether, our findings support that GATA6 expression defines a tumor cell state that is both antigen-presenting and immune-permissive, and that MEKi transiently enhances this immunogenicity before inducing a GATA6 mesenchymal phenotype. low These insights provide the rationale for therapeutic strategies such as MEKi+HDACi to maintain GATA6 expression and prolong tumor sensitivity to immune attack.\n2. The induction of MHCI in GATA6+ pancreatic cancer cells upon MEKi treatment was shown in a couple of KPC cell lines. However, this does not appear robust enough or whether similar dynamics occur in human PDAC cell lines across different molecular subtypes (PDAC cell lines known to belong to classical subtype vs. basal-like).\nResponse:\n Thank you for the constructive advice. In the revised version, we have expanded our cell line panel for the in vitro experiment. 8 murine PDAC cell lines (4 GATA6high vs 4 GATA6 ) were included for the in vitro MEKi and/or HDACi experiments (Fig. 2c; low Fig. 4d-f). This broader panel allows us to more robustly assess the association between GATA6 expression and MHCI induction. To further validate the functional role of GATA6, we employed two complementary genetic approaches in GATA6high murine cell lines: (1) an auxin-inducible degradation (AID) system in the 110299 cell line (Fig. 2f–h; Fig. S2c–g), and (2) CRISPR--Cas9 mediated knockout in the 2838c3 cell line (Fig. 2d–e; Fig. S2b). In both models, GATA6 loss abrogated MEKi-induced MHCI upregulation, strengthening our conclusion that GATA6 is required for this effect.\n Regarding human tumor cell responses, instead of using commercial human cell lines, we analyzed a panel of 15 patient-derived xenograft (PDX) models. In the revised manuscript, we now include the staining of KRT81, a basal-like subtype marker, to classify these models into classical or basal-like subtypes (Fig. S5b). This allows us to relate subtype identity to MHCI responses upon MEKi treatment. Our findings show that MEKi-induced MHCI upregulation occurs primarily in GATA6+ (classical) tumors, supporting the relevance of our observations in both mouse and human systems.\n3. In figure 3, the analysis seems limited as it tests GATA6-dependent MHCI expression and T-cell cytotoxicity mainly in one cell line each. Expanding this to more diverse cell lines could strengthen the conclusions drawn. It is not clear whether differential expression of MHCI is functionally important to determine anti-tumor activity.\nResponse:\n We thank the reviewer for this important suggestion. In response, the original Figure 3 has been reorganized and is now presented as Figure 5 in the revised manuscript.\nTo address the concern about limited cell line diversity, we expanded our co-culture experiments to include a total of four GP-expressing cell lines (2 GATA6high vs. 2 GATA6 ). We observed that both GATA6high GP-expressing cell lines respond to low MEKi and/or HDACi by upregulating MHCI expression to a greater extent than the GATA6 cell lines (Fig. 5d), and that this increase correlates with enhanced T cell- low mediated anti-tumor cytotoxicity (Fig. 5e-f). We deduced that MHCI upregulation contributes at least in part to the observed increase in anti-tumor cytotoxicity.\nTo further support the association between MHCI and tumor cytotoxicity, we performed mIF analysis in the CKP mouse model treated with MEKi and/or HDACi (Fig. 7c-e). Our findings show that GzmB+ cells were more frequently found in close proximity to MHCI+ tumor cells, which were undergoing apoptosis (Cl casp3+). This suggests that the enhanced cytotoxic cell-mediated cytotoxicity was specifically directed at MHCI+ tumor cells, further reinforcing the link between MHCI expression and anti-tumor activity.\nTo validate the role of GATA6 in the MEKi-induced MHCI and T cell cytotoxicity in vivo, we employed a targeted GATA6 protein degradation system using the orthotopic transplantation model of AID-GATA6-KI cells (Fig. 3e-h; Fig. S3d-f). With the AID-GATA6-KI system, we were able to reduce GATA6 protein expression after tumor establishment, and GATA6 levels remained low throughout the treatment period upon auxin administration (Fig. 3g; Fig. S3d). We found that GATA6 degradation significantly impaired MEKi-induced tumor suppression in the 110299AID-GATA6 orthotopic model (Fig. 3f). Additionally, the MEKi-induced upregulation of tumor MHCI expression, CD8 infiltration, and anti-tumor cytotoxicity were all markedly reduced (Fig. 3h), highlighting GATA6’s essential role in MEKi-induced anti-tumor responses.\nNotably, MEKi-induced CD4 infiltration was unaffected by GATA6 degradation (Fig. S3f), suggesting that GATA6 specifically mediates the MEKi effect via the MHCI-CD8 pathway.\n4. The introduction of HDAC inhibitors is abrupt and their role in the combinatory treatment effect alongside MEKi is not sufficiently explained.\nResponse:\n In this study, HDAC inhibitors were chosen because HDACs play a key role in promoting EMT, and HDAC inhibition has been used in various cancer models to counteract EMT. In PDAC, Domatinostat has been shown to promote epithelial gene expression via a BRD4- and MYC-dependent mechanism 3.\nSince MEKi treatment consistently reduces GATA6 levels, probably via EMT induction (Fig. S5e), we hypothesized that HDACi could restore GATA6 expression. Indeed, we demonstrated that HDACi treatment largely restores GATA6 expression in vitro (Fig. 4d,e) and in vivo (Fig. 6e,f), significantly improving tumor control and mouse survival (Fig. 6b,c). Besides, we also demonstrated in the revised manuscript that HDACi, indeed, counteracts the EMT induced by MEKi (Fig. S5e).\nWe have clarified these findings and their rationale in the revised manuscript.\nImportantly, the combination of MEKi and HDACi was used as a proof-of-principle strategy to restore GATA6+ tumor cells population, thereby enhancing treatment responsiveness. This supports the concept that targeting tumor cell plasticity can improve therapeutic outcomes.\n5. When combo-treatment is done in PDX models (Figure 4), is the therapeutic effect mainly through MHCI-T cell mediated cytotoxicity? If so, PDX models in immune-compromised or -deficient hosts show no obvious difference since they lack adaptive immune system?\nResponse:\n The purpose of the PDX model is to demonstrate that, in human tumor cells, MEKi- induced MHCI upregulation occurs specifically in GATA6+ tumor cells, as observed in the murine cell line models. In the PDX model, MEKi treatment did not result in significant tumor suppression. Since the PDX model consists of immunodeficient nude mice that lack functional T cells, the observation of higher MHCI expression on GATA6+ cells without a corresponding increase in apoptotic cells suggests that T cells are essential for MHCI-mediated cytotoxicity.\n6. Figure 5e data seems contradictory with what the authors showed so far. MEKi treatment did not up-regulate MHCI expression. HDACi treatment didn’t seem to retain GATA6 positive population either. Only combo context resulted in up-regulation of MHCI (no change in GATA6 positive population). This also raise a concern whether the main hypothesis was thoroughly tested and the conclusion is supported by robust data.\nResponse:\n The original Figure 5 has now been reorganized and is presented as the revised Figure 6. In the experiment originally shown in Fig. 5e (the revised Fig. 6f), we did not observe an increase in MHCI expression with MEKi treatment alone. This outcome is consistent with our mechanistic model: while MEKi upregulates MHCI expression in GATA6+ tumor cells, it simultaneously reduces the proportion of GATA6+ cells, likely through induction of epithelial-to-mesenchymal transition (EMT), as demonstrated in Fig. 4e and Fig. S5e. As a result, the overall MHCI signal remains unchanged or even reduced at the population level.\nIn contrast, HDACi treatment counteracts MEKi-induced EMT and thereby helps preserve the GATA6+ tumor cell population (Fig. 4e). Although HDACi alone does not significantly increase MHCI expression, it restores a GATA6+ epithelial cell state, which is more responsive to MEKi-induced MHCI upregulation. This mechanistic rationale led us to hypothesize that the combination of MEKi and HDACi would synergistically enhance MHCI expression.\nIndeed, this hypothesis is supported by our data: combination treatment significantly increased MHCI expression and enhanced anti-tumor cytotoxicity across multiple models (Fig. 4, 5, 6, and 7). We have also revised the schematic diagram in Figure 8 to better illustrate the proposed mechanism, integrating the dynamic regulation of GATA6 and MHCI in response to therapy.\nTaken together, our data robustly support the conclusion that GATA6 defines a tumor cell state that is both MHCIhigh and immune-permissive, and that MEKi+HDACi combination therapy maximizes MHCI expression by preserving GATA6+ cells, while enhancing their immunogenicity.\nMinor points\n1. In Figure 3a, the figure is not complete.\nResponse:\n We apologize for the incomplete figure, which was likely due the file compression during the submission process. We have carefully reviewed and corrected the figure in the revised manuscript, and we confirm that the complete version is now included.\n2. In figure 4, the graphs do not have some information such as y-axis titles and units.\nResponse:\n We also apologize for the missing axis titles and units in Figure 4, which again may have resulted from formatting or compression issues. In the revised manuscript, we have ensured that all graphs in Figure 4 now include complete axis labels and units for clarity and accurate interpretation.\n3. In Figure 4, for the PDX experiments, the authors used mocetinostat as HDACi, and then switched to dometinostat. Any reason?\nResponse:\n Domatinostat was used for the subsequent experiments because it shows greatest magnitude of MHCI upregulation upon combination with MEKi in the in vitro experiment (Fig. S5f-g).\n4. Figure 5e & 5g, it is questionable whether it accurately counted GATA6+ and MHCI+ cells. The positive cell population ranges only 2-8% in the entire population in a given area?\nResponse:\n The original Figure 5 has now been reorganized and is presented as the revised Figure 6. We have carefully checked our quantification methods and confirm that the reported positive cell populations are accurate. The percentages refer to the proportion of cells positive for GATA6 or MHCI out of the total number of cells present in the entire tumor tissue section. Given the heavy stromal infiltration in PDAC, it is common for stromal cells (including immune cells and fibroblasts) to constitute the majority of the tumor bulk, ranging from 50% to 80%, leaving only about 20% as tumor cells. Among these, GATA6+ tumor cells account for 2% to 8% of the tumor cell population, which is consistent with previous findings in PDAC patient cohorts, where the GATA6+ subtype represents only a subset of PDAC tumors.\nTo improve clarity, we have revised the corresponding figure legends in the revised manuscript (Fig. 6e, f) explicitly define the “positive percentage” as the percentage of positive cells among all cells in the tumor tissue."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have sufficiently addressed my original concerns.\nOne additional concern is over Reference 35, which is bioRxiv from 2022. It is unclear whether Nature allows non-peer review references. Additionally, the age of this reference is also concerning, because it has not been officially published for over 3 years. I would recommend that essential data be added as supplemental for this manuscript or anything associated with that reference be removed as it has not been adequately peer reviewed.", "reviewer_2": "The authors exhaustively addressed all critical comments from my and other reviewers. I believe the manuscripts is substantially improved and can be an exciting contribution to the field.", "reviewer_3": "The authors have done a nice job addressing the concerns from the initial review. They’ve clarified the writing, added important new experiments (including the GATA6 knockout/AID models and CD8 depletion studies), and expanded both the cell line and patient tumor data to strengthen the conclusions. The rationale for including HDAC inhibition is now much clearer, and they’ve been transparent about its limitations.", "reviewer_4": "In the revised manuscript, the authors have provided more robust data to support GATA6-dependent MHCI expression in the context of MEK inhibition. The new data clearly strengthen the conclusion of the revised manuscript. Although the authors effectively showed that GATA6 expression is associated with a more immunogenic basal state, GATA6 does not seem to be responsible for up-regulation of MHCI expression in the basal state as shown in Figure 2d-h. GATA6 knock-outs or degradation have no effect on the basal level of MHCI, while GATA6-high vs. low cells showed a trend of MHCI expression difference. This difference should be clearly documented and acknowledged.\nThe authors have addressed all other concerns in the revised manuscript."}, "author_response": "Thank you for your comment and suggestion. We have now replaced Reference 35 with a recently peer-reviewed and published article:\nGodfrey LK, et al. Pancreatic cancer acquires resistance to MAPK pathway inhibition by clonal expansion and adaptive DNA hypermethylation. Clin Epigenet 16, 13 (2024).\nWe sincerely thank the reviewer for the positive comments.\nWe sincerely appreciate the reviewer’s positive feedback.\nWe thank the reviewer for the thorough evaluation of our data and the positive comments."}]

Decision letter