Overcoming Adaptive Resistance to KRASG12D Blockade in Pancreatic Cancer through Vertical Pathway Inhibition

Qingxiang Lin; Alvin A Morales-Giron; Conrad Sander; Jacquelyn M Curtis; Haley Barnes; Parasvi S Patel; Ferran Fece de la Cruz; Jakob M Riedl; Hiroyuki Matsubara; Hajime Nakamura; Andrew S Liss; Ryan B Corcoran

doi:10.1158/1078-0432.CCR-25-1788

. 2026 Mar 9;32(11):2279–2292. doi: 10.1158/1078-0432.CCR-25-1788

Overcoming Adaptive Resistance to KRAS^G12D Blockade in Pancreatic Cancer through Vertical Pathway Inhibition

Qingxiang Lin ^1,², Alvin A Morales-Giron ^1,², Conrad Sander ^1,², Jacquelyn M Curtis ^1,², Haley Barnes ^1,², Parasvi S Patel ^1,², Ferran Fece de la Cruz ^1,², Jakob M Riedl ^1,^2,³, Hiroyuki Matsubara ^1,², Hajime Nakamura ^1,², Andrew S Liss ^1,², Ryan B Corcoran ^1,^2,^*

PMCID: PMC13223552 PMID: 41801133

Abstract

Purpose:

Oncogenic KRAS mutations are present in >90% of pancreatic ductal adenocarcinoma (PDAC), with KRAS^G12D being the most common. Mutant-selective KRAS^G12D inhibitors (KRASi^G12D) have demonstrated promising initial clinical activity in KRAS^G12D-mutant PDAC. However, adaptive resistance to KRASi constrains efficacy in some tumor types, such as colorectal cancer, in which EGFR-mediated RAS–MAPK pathway reactivation can be targeted to improve response. Some studies have suggested a similar role for EGFR in PDAC, but the mechanisms of adaptive resistance to KRAS inhibition are unclear.

Experimental Design:

Mechanisms of adaptive resistance to KRASi^G12D were investigated in a panel of KRAS^G12D-mutant PDAC models.

Results:

We observed receptor tyrosine kinase (RTK)–driven adaptive reactivation of RAS pathway signaling following KRASi^G12D in PDAC models. EGFR was a primary driver of adaptive RAS–MAPK reactivation in some models but was limited to those with epithelial differentiation. Conversely, adaptive RAS–MAPK reactivation in models with mesenchymal differentiation was primarily driven by FGFR signaling. In clinical PDAC specimens from The Cancer Genome Atlas, EGFR and ERBB3 expression was highly correlated with the expression of epithelial markers, whereas the expression of FGFR1 and mesenchymal markers was correlated. Notably, a RAS(ON) multi-selective inhibitor, which inhibits both wild-type and mutant RAS, abrogated RAS–MAPK reactivation in combination with KRASi in both epithelial and mesenchymal models and led to more consistent antitumor activity compared with combinations of KRASi and EGFR blockade.

Conclusions:

In PDAC, adaptive RAS–MAPK reactivation following KRAS^G12D inhibition can be mediated by different RTKs and influenced by cell state. Combinations of mutant-selective KRASi and RAS(ON) multi-selective inhibitors may represent a promising universal strategy to surmount adaptive resistance in patients with PDAC.

Translational Relevance.

Mutant-selective KRAS inhibitors (KRASi) have shown promising initial efficacy in pancreatic ductal adenocarcinoma (PDAC). However, adaptive resistance through RAS–MAPK reactivation has limited clinical efficacy in other tumor types, such as colorectal cancer, in which EGFR-mediated RAS–MAPK pathway reactivation has been successfully targeted to improve response. Some studies have suggested a similar role for EGFR in PDAC, and this strategy is currently being evaluated in clinical trials, but the mechanisms of adaptive resistance to KRASi inhibition are poorly understood. In this study, we find that adaptive RAS–MAPK reactivation following KRAS^G12D inhibition in PDAC can be mediated by different receptor tyrosine kinases and influenced by cell state. However, we observed that a RAS(ON) multiselective inhibitor in combination with mutant-selective KRASi may represent a more universal strategy to overcome adaptive RAS–MAPK pathway reactivation and improve anti-tumor efficacy in patients with PDAC. Overall, these data support the evaluation of mutant-selective KRASi in combination with RAS(ON) multi-selective inhibitors in future clinical trials.

Introduction

Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer mortality, with a 5-year survival rate of only ∼12% (1). Currently, it is the third leading cause of cancer-related deaths in the United States and is expected to rise to second by 2030 (2). Most patients with PDAC are diagnosed with advanced, disseminated disease, precluding surgery for 80% of patients, and the limited treatment options, as well as the poor response to standard-of-care, leave most patients with a life expectancy of less than 1 year after diagnosis (3).

KRAS mutations are present in >90% of PDAC cases, with G12D (36%), G12V (30%), and G12R (15%) being the most common alterations (4). Wild-type KRAS functions as a binary switch, cycling between an inactive GDP-bound state and an active GTP-bound state (5). In contrast, oncogenic KRAS mutations lock KRAS into a hyperactive GTP-bound state, driving continuous signaling through downstream pathways, such as MAPK. Studies have shown that genetic depletion of KRAS promotes tumor regression and reshapes the tumor microenvironment in numerous preclinical PDAC models (6, 7), affirming KRAS as a putative therapeutic target for patients with PDAC.

Direct pharmacologic targeting of KRAS, which has long been considered “undruggable,” has recently become therapeutically feasible. Covalent inhibitors of the KRAS^G12C protein suppress oncogenic KRAS^G12C signaling, leading to tumor regression in KRAS-driven cancer models and in patients (8). Given encouraging efficacy and tolerability in clinical trials, the US Food and Drug Administration (FDA) has approved two KRAS^G12C inhibitors (KRASi^G12C) for non–small cell lung cancer (NSCLC), including adagrasib (MRTX849; ref. 9) and sotorasib (10–13). However, the clinical efficacy of KRASi^G12C differs across major KRAS-mutant tumor types, and primary and acquired resistance to KRAS^G12C inhibition compromises efficacy and durability. Compared with patients with NSCLC receiving KRASi^G12C, patients with colorectal cancer exhibit lower objective response rates (ORR, 35%–56% in NSCLC vs. 10%–36% in colorectal cancer; refs. 9, 11, 14–17). Our group and others have demonstrated that adaptive feedback signaling leading to RAS–MAPK reactivation can promote primary resistance to KRAS pathway inhibition to varying degrees across tumor types (18–24). In particular, EGFR-mediated adaptive RAS–MAPK reactivation can be an important driver of resistance in colorectal cancer, likely accounting for the lower ORR relative to other tumor types. Importantly, coinhibition of EGFR and KRAS^G12C can abrogate adaptive resistance signaling, leading to improved clinical benefits in colorectal cancer and resulting in the FDA approval of the combinations of adagrasib and cetuximab (CTX), as well as sotorasib and panitumumab (25, 26).

In PDAC, the KRAS^G12C mutation accounts for only ∼1% of the patient population, and initial clinical trials have demonstrated an ORR of as much as 45% (10, 16, 17, 27–29). The KRAS^G12D mutation, the most common KRAS mutation in PDAC, is associated with poor clinical outcomes (31), and the development of agents targeting KRAS^G12D offers a promising therapeutic strategy for patients with PDAC. Early clinical experience with KRAS^G12D-selective inhibitors (KRASi^G12D) has demonstrated an ORR as high as 41% in patients with PDAC (31–34), and several additional KRASi^G12D are under clinical investigation. Given the intermediate clinical responsiveness of patients with PDAC to KRASi^G12C between NSCLC and colorectal cancer (10, 12, 15), it is likely that adaptive resistance mechanisms play a critical role in compromising responses in PDAC. However, although a potential role for EGFR in adaptive resistance, similar to that in colorectal cancer, has been suggested in PDAC (36–38), and this strategy is under evaluation for PDAC in several clinical trials (NCT06227377, NCT07026916), the contributions and mechanisms of adaptive resistance to KRASi^G12D in PDAC are poorly understood. Defining critical mechanisms of primary resistance to KRASi in PDAC could guide the development of novel effective strategies to improve efficacy for patients with PDAC.

Here, we employed a panel of KRAS^G12C- or KRAS^G12D-mutant PDAC models to elucidate mechanisms of adaptive resistance that constrain efficacy following KRAS inhibition. We tested the hypothesis that vertical inhibition of RAS–MAPK reactivation mediated by upstream receptor tyrosine kinase (RTK) feedback signaling could overcome resistance and sustain RAS–MAPK inhibition, thereby enhancing the efficacy of mutant-specific KRASi. We also assessed whether co-targeting wild-type RAS along with mutant KRAS could provide a more universally effective strategy across PDAC subtypes.

Materials and Methods

Cell lines and cell culture

Human cell lines such as MIAPaCa-2 (RRID: CVCL_0428), KP-4 (RRID: CVCL_1338), SW1990 (RRID: CVCL_1723), and HPAF-II (RRID: CVCL_0313) were obtained from the American Type Culture Collection and the Center for Molecular Therapeutics at the Massachusetts General Hosptial (MGH) Cancer Center, which routinely conducts cell line authentication testing by SNP and short tandem repeat analysis. Cells were cultured in their optimal medium (Gibco) supplemented with 10% fetal bovine serum (FBS). Detailed RRID information for the cell lines used is provided in Supplementary Table S1.

Reagents and antibodies

MRTX849 and MRTX1133 were purchased from MedChemExpress. Afatinib, infigratinib [BGJ398 (BGJ)], CTX, lapatinib, crizotinib, and linsitinib were purchased from Selleck Chemicals. Panitumumab was purchased from McKesson Pharmaceuticals. The RAS(ON) multiselective inhibitor (also referred to as pan-RASi) RMC-7977 was provided by Revolution Medicines. For in vitro studies, all drugs were prepared in dimethyl sulfoxide (DMSO, Sigma-Aldrich) as a stock solution (10 mmol/L) and stored at −20°C. For in vivo studies, MRTX1133 was formulated in 10% research-grade Captisol (CyDex Pharmaceuticals) in 50 mmol/L citrate buffer pH 5.0 (Teknova; ref. 39), which was protected from light and stored at 4°C for 1 week. MRTX849 was freshly prepared in 10/40/5/45 (%v/v/v/v) DMSO/PEG400/Tween 80/Saline. For in vivo studies, RMC-7977 was prepared using the formulation of 10/20/10/60 (%v/v/v/v) DMSO/PEG 400/Solutol HS15/water (39). Major antibodies against p-ERK (T202/Y204, cat. #4370, RRID: AB_2315112), p-S6 (S235/236, cat. #4858, RRID: AB_916156), p-S6 (S240/244, cat. #5364, RRID: AB_10694233), vimentin (cat. #5741, RRID: AB_10695459), and β-actin (cat. #3700, RRID: AB_2242334) were purchased from Cell Signaling Technology. Antibodies against E-cadherin (cat. #ab40772, RRID: AB_73149) were purchased from Abcam. Details of known drug targets of these inhibitors and antibodies employed are provided in Supplementary Tables S2 and S3.

Cell viability assay

Cells were seeded at 2 to 5 × 10³ cells/well in 96-well plates, depending on the growth rate of the cell lines, and the next day treated with a serial dilution of drugs at the indicated concentrations. A concentration of DMSO equivalent to the amount present in the highest-concentration drug-treated wells was used as a vehicle control. After 72 hours, CellTiter-Glo solution (Promega) was added to the medium, and absorption was measured using a microplate spectrophotometer (Molecular Devices) after a 10-minute incubation at room temperature. Cell proliferation data were fitted using a sigmoid inhibitory Hill function to estimate IC₅₀ and maximal inhibition mediated by treatment (I_max) values (Supplementary Table S1) using GraphPad Prism version 8.

Colony formation assay

Cells were seeded at low cell density (3 × 10² to 2 × 10³) in 6-well plates and treated with MRTX1133 or MRTX849, or in combination with BGJ, CTX, or RMC-7977 for 9 to 12 days, with drugs refreshed every 3 days. Generally, combinations with BGJ, CTX, and RMC-7977 were assayed in triplicate and in parallel. After that, cells were fixed and stained with a 2% crystal violet solution in 4% formaldehyde for 20 minutes and washed with phosphate-buffered saline (PBS) twice. After drying, the plates were scanned, and the cell colony area was quantified using ImageJ software (NIH) and normalized by controls.

Western blot analysis

Cells were harvested using RIPA lysis buffer containing Halt protease and phosphatase inhibitor cocktails (Thermo Fisher Scientific) on ice. The cell lysates were vortexed vigorously 3 times every 10 minutes, followed by centrifugation (220 g, 20 minutes, 4°C). The supernatant was collected, and the protein concentration was quantified using a BCA kit (Thermo Fisher Scientific). Equivalent amounts of protein from each sample were resolved on 4% to 12% Bis-Tris gradient gels (Invitrogen) and, after protein separation, transferred to polyvinylidene difluoride membranes (Millipore). Membranes were blocked with 5% BSA (Boston BioProducts Inc.) in TBST (PBS containing 0.1% Tween 20, Santa Cruz Biotechnology) for 1 hour, followed by incubation with primary and secondary antibodies (5% BSA in TBST, Supplementary Table S3). The chemical luminescence signal from the membranes was acquired using an ECL substrate kit (Thermo Fisher Scientific) and a gel imaging system (GenSystem). β-Actin was used as a loading control. Densitometry analysis was performed using ImageJ.

Quantitative real-time PCR

RNA extraction was conducted using the RNeasy Kit (QIAGEN) per the manufacturer’s protocol. Reverse transcription was performed using the qScript cDNA SuperMix (Quantabio). qPCR analysis was performed using the TaqMan Gene Expression Master Mix (Thermo Fisher Scientific) on the LightCycler 480 System (Roche Diagnostics International AG). Human primers of TaqMan Gene Expression Assays were purchased from Thermo Fisher Scientific, including DUSP6 (Hs04329643_s1), ETV4 (Hs00383361_g1), ETV5 (Hs00927578_g1), SPRY2 (Hs01921749_s1), SPRY4 (Hs01935412_s1), VIMENTIN (Hs00958111_m1), SNAI1 (Hs00195591_m1), and CDH1 (Hs01023895_m1). β-Actin (4326315E, Thermo Fisher Scientific) was used as an endogenous control.

Gene coexpression profiling interactive analysis

The Gene Expression Profiling Interactive Analysis (GEPIA, http://gepia.cancer-pku.cn/) database (40, 41) was used to analyze and visualize gene coexpression for 179 patient tumors with PDAC publicly available from The Cancer Genome Atlas (TCGA). This pancreatic adenocarcinoma dataset is found under Tissue Samples within the Data Sources tab on the GEPIA2 website.

Animal studies

All procedures were approved in advance by the Institutional Animal Care and Use Committees of the MGH. Tumors were established by subcutaneous injection of 3 to 5 ×10⁶ cells in 1:1 serum-free PBS/Matrigel (Corning Life Sciences) into the flank of 6- to 8-week-old female nude mice (Charles River Laboratory) housed under pathogen-free conditions. When tumor volumes reached 200 to 250 mm³, mice were allocated into treatment groups (n = 7–9/treatment group) of equivalent mean tumor volumes and given indicated drugs. Control mice received no treatment. Drugs were administered for approximately 3 to 5 weekly cycles. The treatments included MRTX1133 (30 mg/kg) intraperitoneally daily or MRTX849 (50 mg/kg) orally daily, or RMC-7977 (5 mg/kg) orally daily, both of which consisted of 5 days of administration in each cycle and a 2-day drug holiday before the next cycle; CTX (0.25 mg/dose) intraperitoneally twice per week; or indicated paired combinations (CTX+MRTX1133, CTX+MRTX849 or MRTX1133+RMC-7977, MRTX849+RMC-7977). Tumor volume was measured at least twice weekly by caliper and calculated as follows: length² × width × 0.5. Body weight was measured at least twice weekly to monitor overt toxicity. Once tumors reached the protocol tumor volume limit (TVL) of 2,000 mm³, any dimension reached 20 mm, or poor health conditions occurred, mice were removed from the study, and the day of sacrifice was recorded for the Kaplan–Meier plot. For pharmacodynamic studies, nude mice bearing tumors of approximately 400 to 500 mm³ were treated with the drugs mentioned above for continuous 6 days, and tumors were harvested 4 hours after the last dose, followed by signaling analysis.

Statistical analysis

Results are shown as mean ± standard deviation (SD) or mean ± standard error of the mean. P values ≤ 0.05 were considered statistically significant. Groupwise comparisons were determined by two-sided, unpaired Student t test with Welch’s correction or one-way ANOVA with multiple comparisons wherever necessary to account for unequal SD between groups. The time of tumor progression to the protocol TVL of 2,000 mm³ or poor health conditions was assessed using Kaplan–Meier analysis as implemented in Prism GraphPad version 8.

Results

RAS–MAPK signaling is reactivated following KRAS^G12D inhibition

To investigate the effects of KRAS^G12D inhibition on cell proliferation, a panel of 14 PDAC models harboring the KRAS^G12D mutation were treated with MRTX1133, a potent, noncovalent KRAS^G12D-selective inhibitor that has demonstrated preclinical efficacy in KRAS^G12D-mutant PDAC models (39) and was evaluated in a phase I/II clinical trial (ClinicalTrials.gov ID: NCT0573706; Fig. 1A; Supplementary Table S1). As expected, MRTX1133 inhibited cell proliferation in the majority of KRAS^G12D-mutant lines but did not significantly repress the proliferation of PDAC cell lines harboring other KRAS mutations (G12V, G12R) or melanoma cells with NRAS (Q61R, Q61K, Q61L) or HRAS (G13D) mutations (Supplementary Fig. S1A–S1C, Supplementary Table S1) at concentrations lower than 1 μmol/L. Although the IC₅₀s (the concentrations required to achieve 50% of the maximal inhibitory effect) for all KRAS^G12D-mutant cell lines fell in a similar range, the maximal inhibitory effect (I_max) at 1 μmol/L varied substantially between models, indicative of differences in sensitivity to KRAS^G12D inhibition. Regardless of sensitivity, MRTX1133 led to near-complete inhibition of ERK phosphorylation at 4 hours in a concentration-dependent manner in KRAS^G12D-mutant PDAC lines (Fig. 1B; Supplementary Fig. S2).

Figure 1. — Effects of KRAS^G12D inhibition on a panel of KRAS^G12D-mutant PDAC cell lines. A, Effect of MRTX1133 on PDAC cell proliferation. A panel of pancreatic cancer cell lines (n = 14) with KRAS^G12D mutation were treated for 72 hours with a concentration titration of MRTX1133, and cell viability was measured by CellTiter-Glo. Curves represent fitted estimations of the data using a sigmoid inhibitory Hill function to estimate IC₅₀s and I_max using Prism GraphPad version 8. Data shown are mean ± SD. B, Western blot analysis of MRTX1133-mediated expression changes in RAS–MAPK signaling in KRAS^G12D-mutant PANC-1, SW1990, and KP-4 cells treated with a range of drug concentrations (0–300 nmol/L) for 4 hours. C, Reactivation of RAS signaling following KRAS^G12D inhibition. Three KRAS^G12D-mutant PDAC cell lines were treated with MRTX1133 (100 nmol/L) for 0 to 96 hours. Western blot analysis was performed to probe phospho-ERK to represent the dynamics of RAS–MAPK activity.

Prior studies from our group and others indicate that KRASi^G12C induce adaptive feedback of RAS–MAPK pathway signaling in a variety of KRAS^G12C-mutant preclinical models, including a single KRAS^G12C-mutant PDAC cell line, driving adaptive resistance to KRAS^G12C inhibition (5, 18). To determine whether reactivation of RAS–MAPK signaling is also observed following KRAS^G12D inhibition in PDAC, we assessed the signaling response to MRTX1133 over 4 to 96 hours, with replenishment of both media and inhibitor every 24 hours (Fig. 1C; Supplementary Fig. S3A). KRAS^G12D-mutant PDAC lines, regardless of their sensitivity, showed rapid and robust RAS–MAPK reactivation starting at 24 to 72 hours from the nadir of maximal MAPK inhibition at 4 hours. Collectively, these data suggest that rapid RAS–MAPK signaling rebound following KRAS^G12D inhibition is common in PDAC and may compromise efficacy, similar to what has been observed with KRAS^G12C inhibition (5, 18).

RTK-mediated adaptive signaling is dependent on the cell differentiation state

Prior studies have suggested that feedback activation of RTK signaling may play a dominant role in driving adaptive feedback to inhibitors of the RAS–MAPK pathway (5, 19, 42–44). EGFR signaling has been reported to drive MAPK feedback reactivation in colorectal cancer models (18, 37, 45), and inhibition of EGFR signaling with an anti-EGFR antibody in combination with BRAF or KRASi^G12C has led to improved outcomes in patients with colorectal cancer and is now FDA-approved (25, 26). Prior studies have also suggested that EGFR may play a similar role in KRAS^G12D-mutant PDAC models (38, 45). Indeed, one study suggested that co-targeting of EGFR and KRAS^G12D could enhance in vivo anti-tumor activity in several PDAC models (38), possibly by inducing deeper and more durable MAPK signaling suppression.

To better define whether EGFR or other RTKs may play a critical role in adaptive resistance to KRAS inhibition in PDAC, we evaluated the potential of combined inhibition of EGFR and mutant KRAS in a panel of KRAS^G12D-mutant PDAC cell lines, as well as one KRAS^G12C-mutant PDAC line, across a representative distribution of clinical subtype classification, including ∼80% classical (epithelial) and ∼20% basal-like (mesenchymal) PDAC tumors (46). The majority of cell lines (n = 8) showed enhanced suppression of viable cell number with combined KRAS and EGFR inhibition using the anti-EGFR antibody CTX compared with each single agent alone [Fig. 2A (middle/bottom rows)]. In contrast, a subset of cell lines (n = 4) did not exhibit an added benefit of coinhibition of EGFR and KRAS relative to KRASi alone [Fig. 2A (top row)]. Interestingly, cell lines that showed increased sensitivity to KRAS and EGFR coinhibition were found to express higher levels of the epithelial marker E-cadherin at both mRNA and protein levels (Fig. 2B; Supplementary Fig. S4A and S4B), whereas cell lines that were not sensitive to KRAS and EGFR coinhibition were observed to express higher levels of mesenchymal markers (vimentin, Zeb1, Snail) while expressing low or undetectable levels of E-cadherin (Fig. 2B; Supplementary Fig. S4A and S4B). A cell line generated from a xenograft derived from a patient with KRAS^G12D-mutant PDAC (MGH1247) was characterized as epithelial and also demonstrated increased in vitro efficacy with coinhibition of KRAS and EGFR (Supplementary Fig. S4C). Interestingly, at baseline, KRAS^G12D-mutant mesenchymal lines (n = 3) showed significantly lower maximal inhibition (I_max) to KRAS^G12D inhibition alone than epithelial lines (n = 10), suggesting a more resistant phenotype in the mesenchymal cell population overall (Fig. 2C).

Figure 2. — FGFR and EGFR mediate distinct adaptive mechanisms of mutant-selective KRASi-mediated signaling rebound in different PDAC subtypes. A, A panel of PDAC cells with KRAS^G12C (n = 1 cell line) or KRAS^G12D (n = 11 cell lines) mutations was treated with MRTX849 or MRTX1133, respectively (M; 10, 30, 100, and 300 nmol/L), alone or combined with CTX (10 μg/mL) at indicated concentrations for 72 hours. Cell viability was quantified by the CellTiter-Glo assay. B, Western blot analysis of EMT markers in untreated KRAS^G12C-mutant (n = 1 cell line) and KRAS^G12D-mutant (n = 14 cell lines) PDAC cell lines. Cell subtypes, mesenchymal or epithelial, were characterized. A subset of PDAC lines (n = 4) exclusively expressed mesenchymal markers (vimentin and Zeb1) while not expressing the epithelial marker E-cadherin and were identified as mesenchymal lines, including MIAPaCa-2, KP-4, PANC-1, and SW1990. The rest of the cell lines were characterized as epithelial, expressing E-cadherin. β-Actin was used as a loading control. C, Comparison of maximal inhibition indices (I_max) in 3 mesenchymal and 10 epithelial KRAS^G12D-mutant PDAC lines. D, Heatmap of *DUSP6* expression across KRAS-mutant PDAC cell lines. A library of KRAS-mutant PDAC cell lines (n = 13) was treated with KRASi (MRTX849 for KRAS^G12C-mutant MIAPaCa-2, MRTX1133 for other KRAS^G12D-mutant lines) combined with pan-FGFRi (BGJ, 1 μmol/L), an EGFR monoclonal antibody (CTX, 10 μg/mL), or other RTK inhibitors (Supplementary Table S2) for 72 hours. *DUSP6* was probed to represent RAS activity at the mRNA level using quantitative real-time PCR analysis and β-actin as a loading control. In each line, the expression quantification of *DUSP6* for each treatment was normalized to that of KRASi treatment at 72 hours. Mesenchymal and epithelial PDAC cell lines were characterized above. E, Phospho-ERK was probed to represent RAS protein activity in mesenchymal KRAS-mutant PDAC cell lines after indicated drug treatment, including one KRAS^G12C-mutant line MIAPaCa-2 and two KRAS^G12D-mutant PDAC lines KP-4 and PANC-1. The other treatments included DMSO control (CTL), KRASi (MRTX, MRTX849, or MRTX1133, 100 nmol/L), inhibitors of RTKs (1 μmol/L), and paired combinations, for 72 hours (except for the “4-hour” MRTX849 treatment in a MIAPaCa-2 sample). The inhibitors of RTK included afatinib (AFA, EGFR/HER2/HER4 inhibitor), BGJ (FGFR1-3 inhibitor), crizotinib (CRI, c-Met/ALK inhibitor), CTX (EGFR antibody), lapatinib (LAP, EGFR/HER2 inhibitor), and linsitinib (LIN, IGF-1R inhibitor). Expression quantification was normalized to that of KRASi treatment at 72 hours (normalized quantification is shown below each band). Data shown are mean ± SD. *, P < 0.05; **, P < 0.01; ***, P < 0.001; NS, not significant.

The differential responsiveness across models prompted us to investigate the molecular mechanisms mediating adaptive vertical RAS signaling reactivation following KRAS inhibition in different cell subtypes (epithelial vs. mesenchymal). Using a panel of 13 KRAS^G12C- or KRAS^G12D-mutant PDAC cell line models (four mesenchymal and nine epithelial), we analyzed RAS–MAPK pathway activity by quantifying the expression of several key regulated transcripts (DUSP6, SPRY2, SPRY4, ETV4, ETV5; ref. 18) under KRAS inhibition alone or combined with clinically relevant RTK inhibitors or antibodies (Supplementary Table S2). Responses varied across cell lines, with selective inhibitors affecting MAPK activity differently when combined with KRASi (Supplementary Figs. S5 and S6). Interestingly, coinhibition of RTK and KRAS showed a cell state–specific pattern in either mesenchymal or epithelial models. The pan-FGFR inhibitor (pan-FGFRi) BGJ combined with KRASi suppressed RAS–MAPK activity, measured by the expression of DUSP6 and other substrates downstream of the MAPK pathway, specifically in mesenchymal models (Fig. 2D; Supplementary Figs. S5 and S6A), whereas the anti-EGFR antibody CTX or two pan-HER inhibitors (afatinib and lapatinib) combined with KRASi did so in the majority of epithelial models (Fig. 2D; Supplementary Figs. S5 and S6B). Similarly, the combination of a different anti-EGFR antibody, panitumumab, with MRTX1133 also did not reduce MRTX1133-mediated MAPK rebound in mesenchymal PDAC cell lines (Supplementary Fig. S7A), suggesting alternative RTK(s) mediating this RAS–MAPK reactivation of the mesenchymal lines rather than EGFR. Western blots also confirmed that combined BGJ + MRTX849 or BGJ + MRTX1133 uniquely suppressed phospho-ERK expression to lower levels in three KRAS^G12C- or KRAS^G12D-mutant mesenchymal cell lines compared with MRTX849 or MRTX1133 alone, whereas inhibition of other RTKs combined with KRASi did not (Fig. 2E). Notably, we did not observe a consistent upregulation of EGFR or FGFR following KRASi across all models (Supplementary Fig. S7B–S7D). This is consistent with prior observations and a previously proposed mechanism wherein RTK-mediated adaptive signaling through the RAS–MAPK pathway can be driven by an increased ability of activated RTKs to engage and activate signaling components of the RAS–MAPK pathway and is not exclusively dependent on an upregulation of RTK activity.

To test whether cell state–specific inhibition of different RTKs in combination with KRAS inhibition could enhance drug efficacy, we conducted 3-day cell proliferation and longer-term colony formation assays in both mesenchymal and epithelial PDAC lines and a cell line generated from a patient-derived xenograft (PDX). In all four mesenchymal models, neither KRASi alone nor combined CTX + KRASi led to a significantly increased suppression of cell proliferation compared with single agents, whereas pan-FGFRi combined with KRASi significantly decreased cell proliferation (Fig. 3A and B; Supplementary Fig. S8A; Supplementary Table S4). In KRAS-mutant epithelial lines and a PDX-derived epithelial model, MRTX1133-mediated KRAS inhibition significantly reduced cell proliferation, and CTX combined with MRTX1133 further diminished cell proliferation (P < 0.001; vs. MRTX1133 alone), whereas combined BGJ + MRTX1133 did not (P > 0.05; vs. MRTX1133 alone, Fig. 3C and D; Supplementary Fig. S8B–S8D). Accordingly, signaling analyses confirmed that coinhibition of FGFR and KRAS downregulated RAS–MAPK rebound specifically in mesenchymal lines (n = 4), whereas cosuppression of EGFR and KRAS did so only in epithelial lines (n = 4) compared with KRAS inhibition alone (Fig. 3E and F).

Figure 3. — Distinct suppressive effects in different EMT subtypes in PDAC. **A–D,** Representative images and quantification of colony formation in four mesenchymal (A and B, KRAS^G12D-mutant PANC-1, KP-4, SW1990, and KRAS^G12C-mutant MIAPaCa-2) and two epithelial (C and D, KRAS^G12D-mutant Panc 04.03 and SU86.86) PDAC lines treated with KRASi (MRTX1133 or MRTX849; 300 nmol/L for mesenchymal lines and 30 nmol/L for epithelial lines), CTX (EGFR antibody, 10 μg/mL), BGJ (pan-FGFRi, 300 nmol/L), or paired combinations (n = 3 wells/treatment). Drugs were refreshed every 3 days, and treatment continued for 9–12 days. Cells were fixed and stained with crystal violet, and areas of purple colonies were quantified using ImageJ. Data shown are mean ± SD. NS, not significant. E and F, Western blot analysis was used to compare cell responses of RAS signaling to MRTX + CTX versus MRTX + BGJ in four (E) mesenchymal and four (F) epithelial PDAC lines. PDAC cell lines were treated with KRASi (MRTX and MRTX1133 for KRAS^G12D-mutant lines and MRTX849 for the KRAS^G12C-mutant line), CTX (10 μg/mL), BGJ (1 μmol/L), or combined for 72 hours, as indicated. Phospho-ERK was probed to represent the activity of RAS–MAPK signaling.

To assess the potential clinical relevance of these findings, we evaluated the expression levels of FGFR or ErbB family members and epithelial or mesenchymal markers in the TCGA database of 179 clinical PDAC tumor specimens (http://gepia.cancer-pku.cn/; ref. 41). Clinical PDAC tumors demonstrated clear, positive correlations between FGFR1 expression (P < 0.05) and well-known mesenchymal markers that encode N-cadherin, Zeb1, vimentin, Snail, and a negative correlation between the expression of FGFR1 and the epithelial marker E-cadherin (P < 0.01, Fig. 4A), suggesting a clinically significant association of FGFR1 and the mesenchymal phenotype. However, FGFR2 or FGFR3 was less consistently correlated with certain mesenchymal markers in samples of patients with PDAC (Fig. 4A). By contrast, clinical PDAC tumor expressions of EGFR and HER3 were significantly correlated with the expression of the epithelial marker that encodes E-cadherin (P < 0.05), and HER3 expression was negatively correlated with all mesenchymal markers (P < 0.05, Fig. 4B), suggesting a potential role for HER members in supporting the epithelial phenotype in PDAC. These correlations imply that the expression of distinct RTKs can correlate with epithelial or mesenchymal differentiation profiles.

Figure 4. — Coexpression profiles of FGFRs or HERs with EMT markers in patients with PDAC. A and B, Coexpression analysis of (A) *FGFRs* (*FGFR* 1–3) and (B) HERs (*EGFR, HER2, HER3*) and five EMT markers in tumors of patients with PDAC; P values and linear correlation coefficients (R) were calculated from GEPIA (http://gepia.cancer-pku.cn/), which analyzes the TCGA sequencing data of patients with PDAC with unknown treatment history. The epithelial marker used here was *Cdh1* (encoding E-cadherin), and the mesenchymal markers were *Cdh2* (encoding N-cadherin), *Zeb1*, *Vim* (encoding vimentin), and *Snai1* (encoding Snail).

Taken together, our data suggest that RTK-mediated feedback adaptation to KRAS inhibition in PDAC can be heterogeneous based on cell states, with FGFR signaling driving adaptive RAS–MAPK reactivation specifically in mesenchymal cells, compared with EGFR in epithelial cells.

Wild-type RAS signaling inhibition reduces feedback reactivation following KRAS inhibition

As PDAC can display diverse cell differentiation states, inhibition of a single RTK, such as EGFR or FGFR only, may not consistently block upstream adaptive signaling changes in response to KRAS inhibition across all PDAC subtypes. As adaptive signaling ultimately converges downstream to reactivate RAS signaling, we hypothesized that coinhibition of mutant KRAS and wild-type RAS as a convergent signaling node could provide a more universal blockade of MAPK feedback reactivation across PDAC subtypes. To test this hypothesis, we combined the mutant-selective KRASi with the RAS(ON) multi-selective inhibitor, RMC-7977, a preclinical tool compound that is representative of the investigational agent daraxonrasib (RMC-6236). These RAS(ON) multi-selective inhibitors target both mutant and wild-type RAS across all RAS isoforms (39, 47, 48). In both mesenchymal and epithelial PDAC models, the combination of mutant-selective KRASi and RAS(ON) multi-selective inhibition mostly showed significantly increased suppression of colony formation compared with each single-agent alone (Fig. 5A–D). Importantly, combined RMC-7977 + MRTX1133 or RMC-7977 + MRTX849 also abrogated phospho-ERK rebound in KRAS^G12D- or KRAS^G12C-mutant models (Fig. 5E and F; Supplementary Fig. S9; Supplementary Table S5). These results suggest that coinhibition of wild-type RAS following mutant-KRAS inhibition can intercept convergent adaptive resistance signaling, regardless of cell subtypes or whether adaptive signaling is driven predominantly by EGFR or FGFR.

Figure 5. — Wild-type RAS inhibition mediated by a RAS(ON) multi-selective inhibitor enhances the efficacy of KRAS inhibition. **A–D,** Representative images and quantification of colony formation in four mesenchymal (KRAS^G12D-mutant PANC-1, KP-4, SW1990, and KRAS^G12C-mutant MIAPaCa-2) PDAC lines and epithelial lines (Panc 04.03, SU86.86, Panc 10.05, and PDX MGH1247). Mesenchymal models were treated with KRASi (MRTX1133 or MRTX849, 100 nmol/L), RMC-7977 [a RAS(ON) multi-selective inhibitor, 30 nmol/L], or paired combinations (n = 3 wells/treatment). Epithelial models were treated with MRTX1133 (30 nmol/L for all, 100 nmol/L for MGH1247), RMC-7977 (30 nmol/L for all, 1 nmol/L for MGH1247), or paired combinations (n = 3 wells/treatment). Medium and compounds were refreshed every 3 days, and treatment continued for 9–15 days. Cells were fixed and stained with crystal violet, and the areas of purple colonies were quantified using ImageJ. Data shown are mean ± SD. *, P < 0.05; **, P < 0.01; ***, P < 0.001; NS, not significant. E and F, Western blot analysis of RAS and mTOR signaling in mesenchymal PDAC cell lines treated with KRASi, RAS(ON) multi-selective inhibitor, or combined for 72 hours at indicated nanomolar concentrations. Phospho-ERK and phospho-S6 were probed to represent the activity of RAS–MAPK and PI3K–mTOR pathways.

Combination of RAS(ON) multi-selective inhibitor and KRASi^G12D or KRASi^G12C significantly improves anti-tumor activity, irrespective of tumor differentiation status

To further compare the combination effects of KRASi with RAS(ON) multiselective inhibition versus EGFR antibody, we conducted in vivo experiments using five PDAC xenograft models, including one PDX model. Given the potential for cell-state dynamics in vivo, tumor cell states (n = 2 tumors/types) were characterized and compared with those of PDAC cell lines cultured in vitro (Fig. 6A). Compared with corresponding cells in vitro, untreated MIAPaCa-2 or KP-4 tumors exhibited similar levels of vimentin, whereas E-cadherin expression was low, suggesting a relatively consistent mesenchymal state in KP-4 and MIAPaCa-2 tumors. In contrast, HPAF-II and MGH1247 tumors expressed higher levels of E-cadherin, suggesting a more epithelial state. However, untreated SW1990 tumors exhibited upregulation of epithelial markers, such as E-cadherin, and evident downregulation of the mesenchymal marker vimentin. These data suggested a potential cell-state shift in SW1990 tumors, transitioning from a mesenchymal state in vitro to an epithelial state in vivo.

Figure 6. — Effects of coinhibition of KRAS and wild-type RAS in KRAS-mutant PDAC tumor models. A, Determination of tumor differentiation status *in vivo*. Non-treated tumors of cell-based KP-4, MIAPaCa-2, SW1990, and HPAF-II models and the patient-derived MGH1247 model were harvested from immunocompromised nude mice (n = 2 tumors/tumor type). Western blot analysis was employed to compare the expression of EMT markers in tumors to *in vitro* expression in cell lines. Panc 04.03 and HPAF-II cells were known controls of the epithelial state, and KP-4 and SW1990 cells were controls of the mesenchymal state, which were also characterized in Fig. 2B. Epithelial and mesenchymal markers used here were E-cadherin and vimentin. **B–F,** Tumor growth curves of KRAS-mutant tumor models, including mesenchymal KP-4 and MIAPaCa-2 tumor models and epithelial SW1990, HPAF-II, and MGH1247 tumor models. Immunocompromised nude mice (n = 7–9 mice/group) bearing subcutaneous tumors were treated with MRTX1133 (30 mg/kg i.p., every day) for KRAS^G12D-mutant models or MRTX849 (50 mg/kg oral, every day) for the KRAS^G12C-mutant model (both of which consisted of 5 days of administration in each cycle and a 2-day drug holiday before the next cycle), RMC-7977 (5 mg/kg oral, every day), CTX (0.25 mg/dose i.p., every 3 days), or paired combinations as indicated. These efficacy experiments were completed on days 28 to 37 after the initiation of dosing. Tumor volume was measured at least twice weekly, and body weight and condition were monitored throughout the treatment period. Data for the mean tumor volume were no longer plotted once the first animal was removed per the protocol TVL of 2,000 mm³ or any dimension of 20 mm, except for the controls of the MIAPaCa-2 model. Data shown are mean ± standard error of the mean (SEM). Groups were compared using one-way ANOVA with multiple comparisons. G, Signaling analysis of tumors (n = 3 tumors/group) treated with indicated drugs for a continuous 6 days. Tumors were harvested 4 hours after the last dose and subjected to Western blot analysis. Phospho-ERK was used to represent the activity of the RAS–MAPK pathway and was quantified based on the band intensity. Data shown are mean ± SD. *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P <0.0001; NS, not significant.

To evaluate the in vivo antitumor activity of combined RAS(ON) multi-selective inhibitors and KRASi, we evaluated KRAS-mutant tumors with different in vivo differentiation statuses (Fig. 6). Tumor-bearing nude mice were treated for at least three cycles consisting of 5 days per week of intraperitoneal injection of MRTX1133 (30 mg/kg, daily) or MRTX849 (50 mg/kg, daily), followed by a 2-day dosing holiday (50) and CTX (0.25 mg/dose, twice per week), oral administration of RMC-7977 (5 mg/kg, daily), or MRTX1133 or MRTX849 in combination with each. A 5 mg/kg dose of RMC-7797 was chosen, as this dose mirrors human exposures that are well tolerated and have lesser adverse events than the full recommended phase II dose of daraxonrasib, to increase the likelihood that a combination with mutant-selective KRASi would be tolerated. For the mesenchymal KP-4 and MIAPaCa-2 models, single agents suppressed tumor progression significantly, and both mesenchymal models showed no added benefit from coinhibition of KRAS and EGFR compared with KRAS inhibition alone but demonstrated a significant benefit from vertical RAS–MAPK pathway inhibition (Fig. 6B and C; Supplementary Fig. S10A; Supplementary Table S6). The epithelial PDAC models, including SW1990, HPAF-II, and MGH1247, showed enhanced and sustained inhibition by co-targeting EGFR and KRAS compared with KRAS inhibition alone. However, no significant difference in efficacy was shown when comparing combined coinhibition of EGFR + KRAS versus pan-RASi + KRASi in those epithelial PDAC models (Fig. 6D–F; Supplementary Fig. S10B; Supplementary Table S7). Importantly, RMC-7977 + MRTX1133 was as effective as or more effective than CTX + MRTX1133 across both tumor differentiation states (Fig. 6B–F) and was well tolerated based on body weight monitoring (Supplementary Fig. S10C–S10G). Signaling analyses of KP-4 and SW1990 tumors (n = 3 tumors/group) treated for 6 days revealed incomplete MAPK suppression by MRTX1133, which was suppressed by MRTX1133 + CTX only in the epithelial SW1990 model but not in the mesenchymal KP-4 model. In contrast, MAPK activity was markedly reduced by RMC-7977 + MRTX1133 universally in epithelial and mesenchymal models (Fig. 6G). Similar to our in vitro findings, phospho-EGFR activity and EGFR expression did not markedly change in treated tumors (Supplementary Fig. S10H). Taken together, these in vivo data suggest that co-inhibition of EGFR and mutant KRAS may be effective only in some PDAC tumors and may be more likely to demonstrate efficacy in tumors that display epithelial differentiation. Conversely, these data support the idea that coinhibition of mutant and wild-type RAS with mutant-selective and RAS(ON) multi-selective inhibitors could provide a more universal strategy to overcome adaptive feedback resistance, warranting further evaluation as a therapeutic strategy for KRAS-mutant cancers.

Discussion

PDAC has the lowest 5-year survival rate among prevalent cancers and remains a devastating clinical problem, lacking effective treatment options (1). Oncogenic KRAS mutations are present in >90% of PDAC diagnoses (4). Direct KRAS inhibition represents a promising strategy to transform PDAC therapy. Although KRAS^G12C mutations are rare (∼1%) in patients with PDAC, initial clinical responses in patients with PDAC receiving KRASi^G12C have been encouraging, with the ORR ranging from 21% to 45% (10, 17, 27–29). KRAS^G12D is the most common KRAS mutation in PDAC and is associated with poor clinical outcomes (30). Early clinical trials of KRASi^G12D, including zoldonrasib (RMC-9805), ASP3082, GFH375, and INCB161734, have also demonstrated promising initial efficacy in PDAC, with response rates as high as 41% (31, 32, 50). Given that adaptive resistance has been shown to constrain the response and durability of KRAS^G12C inhibition (10, 36), we evaluated potential mechanisms of adaptive resistance to KRASi^G12D specifically in PDAC to help guide future therapeutic strategies.

EGFR has been implicated as a primary driver of adaptive resistance to KRAS pathway inhibition in colorectal cancer, and coinhibition of EGFR and KRAS has been shown to abrogate adaptive RAS–MAPK signaling reactivation and improve clinical efficacy (38), leading to the FDA approval of the combination of adagrasib and cetuximab, as well as sotorasib and panitumumab, for patients with KRAS^G12C-mutant colorectal cancer. Although a potential role for EGFR in adaptive resistance to KRAS inhibition in PDAC has also been proposed, the contribution and potential mechanisms of adaptive resistance to KRAS inhibition in PDAC are poorly understood. We found that adaptive RAS–MAPK pathway reactivation was commonly observed across PDAC models following KRASi treatment. Interestingly, we found that co-inhibition of EGFR and KRAS did lead to sustained RAS–MAPK suppression and enhanced efficacy in many PDAC models. However, we observed that the benefit of combined EGFR and KRAS inhibition seemed to be restricted to PDAC models exhibiting epithelial differentiation. By contrast, in PDAC models with mesenchymal differentiation, adaptive feedback signaling seemed to be EGFR-independent and driven primarily by FGFR, and in vitro efficacy and sustained RAS–MAPK inhibition were observed only with combinations of KRASi and FGFRi and not with EGFR co-inhibition. These data align with previous studies in other tumor types suggesting that EGFR/ErbB or non-EGFR/ErbB signaling, such as FGFR signaling, may play a more predominant role in driving adaptive resistance to BRAF or KRAS inhibition in the setting of epithelial versus mesenchymal differentiation, respectively (18, 51). Additionally, expression data from a clinical database of 179 PDAC tumor specimens showed a strong and statistically significant correlation between ErbB family members and epithelial markers, as well as between FGFR1 and mesenchymal markers, providing an important clinical correlation with our preclinical data. Finally, we found that co-inhibition of EGFR and KRAS^G12D led to a significantly enhanced inhibition of tumor growth in epithelial PDAC models in vivo. Taken together, these data suggest that combinations of anti-EGFR antibodies and mutant-selective KRASi, which have been well tolerated so far in other tumor types, could represent a promising strategy for evaluation in PDAC, particularly in tumors with epithelial differentiation. Consistent with this hypothesis, initial clinical data from the G12D-preferring KRAS inhibitor QTX-3034 suggest improved response rates in KRAS^G12D PDAC with QTX-3034 administered with cetuximab versus monotherapy, though patient numbers are still low (52). Evaluation of the epithelial versus mesenchymal differentiation status of patients treated in this and other clinical trials of KRASi and EGFR antibodies in PDAC will be important to assess this hypothesis and to determine whether differentiation status may represent a useful biomarker to select patients for dual KRAS/EGFR therapy.

However, our data suggest that combined EGFR and KRAS inhibition may not be universally effective in all PDAC cases. In particular, we found that combined inhibition of EGFR and KRAS did not enhance RAS–MAPK suppression or in vitro efficacy in PDAC models with mesenchymal differentiation. Similarly, combined EGFR and mutant-selective KRAS inhibition did not significantly improve in vivo antitumor activity in mesenchymal PDAC models. Moreover, the observation that the differentiation status of some PDAC models (e.g., SW1990) can exhibit plasticity also suggests the potential limitations of single RTK targeting. Vertical inhibition at convergent nodes within the MAPK pathway has been proposed as a potential strategy that could provide more universal benefits across tumors with distinct drivers of adaptive resistance (EGFR-dependent or independent) and address intra-tumor heterogeneity with various cell differentiation statuses (5, 18, 51, 53). As RTK-mediated adaptive signaling (whether through EGFR, FGFR, or other RTKs) converges to activate RAS, we evaluated a novel RAS(ON) multi-selective inhibitor—RMC-7977, a preclinical tool compound representative of the investigational agent daraxonrasib—that can inhibit all RAS isoforms (39, 47, 48). Thus, we hypothesized that broad-spectrum RAS(ON) inhibition in combination with mutant-selective KRAS inhibition might provide a more universally effective approach to overcoming adaptive resistance across PDAC models and tumor heterogeneity. Indeed, combined KRAS and RAS(ON) multi-selective inhibition showed significantly improved inhibition of tumor growth in vivo, relative to each agent alone, regardless of epithelial versus mesenchymal differentiation. Although daraxonrasib has demonstrated encouraging single-agent activity in patients with PDAC, with an ORR of ∼20 to 30%, a median progression-free survival of 8.5 months, and a median overall survival of 14.5 months (54), further clinical studies will be needed to determine whether some of the potential on-target toxicities of inhibiting all RAS isoforms might prevent broad-spectrum RAS inhibition with a RAS(ON) multi-selective inhibitor from achieving optimal efficacy in KRAS-mutant tumors as a monotherapy. One intriguing alternative is that RAS(ON) multi-selective inhibitors might represent promising combination partners with selective KRASi and that each in combination might enable enhanced RAS–MAPK suppression and improved anti-tumor efficacy, perhaps even if given at lower and potentially better-tolerated doses (such as those evaluated in this study) than required when administered alone. Our preclinical data suggest that combinations of mutant-selective KRASi and a RAS(ON) multi-selective inhibitor warrant further clinical exploration to determine whether this combination might offer advantages in tolerability and efficacy, and clinical trials evaluating such combinations are currently ongoing (NCT06040541).

This study has several limitations, including a finite number of PDAC models from different differentiation states, although the distribution of models used does approximate the actual relative clinical distribution of cell-state subtypes (46). The cell state–dependent mechanism of adaptive resistance warrants further holistic validation through additional preclinical studies in patient-derived models and proof-of-concept clinical trials. It is also possible that the complex PDAC tumor microenvironment, including cell–cell interactions among cancerous and noncancerous cells (35, 55), as well as sex differences, could potentially influence drug combination efficacy (35, 55), necessitating further systematic investigation. Some studies have reported differing effects of epithelial–mesenchymal transition (EMT) on sensitivity; thus, EMT dynamics, despite being uncommon in our in vivo experiments, across in vitro and in vivo preclinical models and human specimens, as well as the associations between baseline EMT levels and treatment-induced changes, warrant further mechanistic investigation (36, 56). Finally, although this study focuses primarily on adaptive mechanisms of resistance, other mechanisms of primary or acquired resistance have been proposed in PDAC and other tumor types, including alterations in RTK–RAS signaling (5, 18, 19, 51), the emergence of mutations and amplifications in KRAS (13, 15, 31, 38, 42, 43, 57, 58) or other pathway effectors (5, 18, 45, 59, 60), histologic cell state (20, 51, 56, 61–63), YAP1 activation, SMARCB1-MYC, and USP21 deubiquitinase (64–66), the PI3K pathway (38), and interaction with the immune response (35, 55). Thus, the interplay between these potential mechanisms of resistance and the adaptive signaling mechanisms studied herein also requires more extensive evaluation.

In conclusion, our data suggest that adaptive feedback signaling in response to KRAS inhibition driven by RTKs may be an important driver of primary resistance in PDAC. Although combined EGFR and KRAS targeting, which has proven well-tolerated and effective in colorectal cancer, may also represent a promising strategy in PDAC, we find that different RTKs may play a predominant role in adaptive feedback signaling depending on the epithelial versus mesenchymal differentiation status of PDAC. Conversely, our preclinical findings suggest that combinations of selective KRASi and RAS(ON) multi-selective inhibitors could represent a more universal strategy to overcome adaptive resistance. This approach warrants further clinical evaluation in PDAC and potentially in multiple other tumor types that harbor high frequencies of KRAS mutations.

Supplementary Material

Supplementary Figure S1

Supplementary Figure S1. Effect of KRASG12D inhibition on cancer cell lines.

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Supplementary Figure S2

Supplementary Figure S2. Effect of KRASG12D inhibition on KRASG12D-mutant PDAC cell lines.

ccr-25-1788_supplementary_figure_s2_suppfs2.pdf^{(543.5KB, pdf)}

Supplementary Figure S3

Supplementary Figure S3. Effect of MAPK reactivation over 0-96h exposure of MRTX1133 on KRASG12D-mutant PDAC cell lines.

ccr-25-1788_supplementary_figure_s3_suppfs3.pdf^{(781.3KB, pdf)}

Supplementary Figure S4

Supplementary Figure S4. Expression of EMT at baseline.

ccr-25-1788_supplementary_figure_s4_suppfs4.pdf^{(230.3KB, pdf)}

Supplementary Figure S5

Supplementary Figure S5. Quantitative expression of Dusp6 in KRAS-mutant PDAC cell lines treated with combinations of RTK inhibitors and KRASi.

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Supplementary Figure S6

Supplementary Figure S6. Quantitative expression of RAS activity in mesenchymal and epithelial KRAS-mutant PDAC cell lines treated with combinations of RTK inhibitors and KRASi.

ccr-25-1788_supplementary_figure_s6_suppfs6.pdf^{(251.1KB, pdf)}

Supplementary Figure S7

Supplementary Figure S7. EGFR blockade did not inhibit MAPK rebound mediated by KRAS inhibition in certain KRAS-mutant PDAC cell lines.

ccr-25-1788_supplementary_figure_s7_suppfs7.pdf^{(520.5KB, pdf)}

Supplementary Figure S8

Supplementary Figure S8. Combination effects of KRASi+EGFRi vs. KRASi+FGFRi.

ccr-25-1788_supplementary_figure_s8_suppfs8.pdf^{(444.7KB, pdf)}

Supplementary Figure S9

Supplementary Figure S9. Western blot analysis of combined MRTX1133+RMC-7977 in epithelial lines SU86.86 and HPAF-II.

ccr-25-1788_supplementary_figure_s9_suppfs9.pdf^{(129.9KB, pdf)}

Supplementary Figure S10

Supplementary Figure S10. Effects of co-inhibition of KRAS and wild-type RAS in KRASmutant PDAC tumor models.

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Supplementary Table S1

Supplementary Table S1. Cell line information and responses to MRTX1133 for 72h.

ccr-25-1788_supplementary_table_s1_suppts1.pdf^{(72.8KB, pdf)}

Supplementary Table S2

Supplementary Table S2. Inhibitors and their targets

ccr-25-1788_supplementary_table_s2_suppts2.pdf^{(38KB, pdf)}

Supplementary Table S3

Supplementary Table S3. Antibodies employed in the studies.

ccr-25-1788_supplementary_table_s3_suppts3.pdf^{(46.9KB, pdf)}

Supplementary Table S4

Supplementary Table S4. Raw data used in Figure 3, colony area (% of control)

ccr-25-1788_supplementary_table_s4_suppts4.pdf^{(140KB, pdf)}

Supplementary Table S5

Supplementary Table S5. Raw data used in Figure 5, colony area (% of control)

ccr-25-1788_supplementary_table_s5_suppts5.pdf^{(119.9KB, pdf)}

Supplementary Table S6

Supplementary Table S6. Statistical comparison of median survival for KP-4 model.

ccr-25-1788_supplementary_table_s6_suppts6.pdf^{(41.4KB, pdf)}

Supplementary Table S7

Supplementary Table S7. Statistical comparison of median survival for SW1990 model.

ccr-25-1788_supplementary_table_s7_suppts7.pdf^{(47.2KB, pdf)}

Acknowledgments

R.B. Corcoran was supported by NIH/NCI U54CA224068, NIH/NCI Gastrointestinal Cancer SPORE P50 CA127003, NIH/NCI R01CA262805, and a Stand Up To Cancer (SU2C) Colorectal Dream Team Translational Research Grant (SU2C-AACR-DT22-17). This grant is administered by the American Association for Cancer Research, the scientific partner of SU2C. Q. Lin was supported by the Andrew L. Warshaw Research Fund for Pancreatic Cancer (GR1002030) and is partly supported by the American Cancer Society Postdoctoral Fellowship (PF-25-1510348-01-PFCDET).

Footnotes

Note: Supplementary data for this article are available at Clinical Cancer Research Online (http://clincancerres.aacrjournals.org/).

Data Availability

Gene expression analysis was performed on publicly available datasets as described above and is available at http://gepia2.cancer-pku.cn/#dataset. All other data generated in this study, for which a structured repository does not exist, are available within the article and supplementary data files or will be made available upon reasonable request to the corresponding author.

Authors’ Disclosures

A.S. Liss reports financial interest in Flare Therapeutics. R.B. Corcoran reports personal fees and other support from Alterome Therapeutics, Sidewinder Therapeutics, C4 Therapeutics, Cogent Biosciences, Pheon Therapeutics, Nested Therapeutics, Remix Therapeutics, and nRichDx; other support from Erasca and Kinnate Biopharma; grants, personal fees, and other support from Revolution Medicines; personal fees from AbbVie, Genentech, QIAGEN, and Taiho Pharmaceutical; grants and personal fees from Parabilis Medicines; and grants from Novartis, OnKure Therapeutics, and Relay Therapeutics outside the submitted work. No disclosures were reported by the other authors.

Authors’ Contributions

Q. Lin: Conceptualization, data curation, formal analysis, investigation, methodology, writing–original draft, writing–review and editing. A.A. Morales-Giron: Formal analysis, investigation, writing–review and editing. C. Sander: Investigation, writing–review and editing. J.M. Curtis: Formal analysis, investigation, writing–review and editing. H. Barnes: Investigation, writing–review and editing. P.S. Patel: Formal analysis, investigation, writing–review and editing. F. Fece de la Cruz: Conceptualization, formal analysis, supervision, investigation, writing–review and editing. J.M. Riedl: Formal analysis, investigation, writing–review and editing. H. Matsubara: Formal analysis, investigation, writing–review and editing. H. Nakamura: Formal analysis, investigation, writing–review and editing. A.S. Liss: Resources, investigation, writing–review and editing. R.B. Corcoran: Conceptualization, resources, formal analysis, supervision, funding acquisition, investigation, writing–original draft, writing–review and editing.

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