Binimetinib and encorafenib for the treatment of advanced solid tumors with non-V600E BRAF mutations: results from the Phase II BEAVER trial

April A N Rose; Jennifer Maxwell; Emmanuelle Rousselle; Chantel L Mukonoweshuro; Islam E Elkholi; Melody Riaud; Marco Biondini; Erica Cianfarano; Isabel Soria-Bretones; Chantal Tobin; Meghan McGuire; Rhoda W Y Law; Andrew J Elia; Ben X Wang; Ian King; Tong Zhang; Trevor J Pugh; Zaid Saeed Kamil; Marcus Butler; Frances A Shepherd; Natasha B Leighl; Albiruni Abdul Razak; Aaron Hansen; Samuel D Saibil; Philippe L Bedard; Peter M Siegel; Lillian L Siu; David W Cescon; Anna Spreafico

doi:10.1038/s41467-025-68076-7

. 2026 Jan 3;17:1323. doi: 10.1038/s41467-025-68076-7

Binimetinib and encorafenib for the treatment of advanced solid tumors with non-V600E BRAF mutations: results from the Phase II BEAVER trial

April A N Rose ^1,^2,^3,^4,^5,^✉,^#, Jennifer Maxwell ^2,^4,^#, Emmanuelle Rousselle ^2,^4,^#, Chantel L Mukonoweshuro ^2,⁴, Islam E Elkholi ^1,², Melody Riaud ^1,², Marco Biondini ⁶, Erica Cianfarano ⁷, Isabel Soria-Bretones ^1,^2,⁵, Chantal Tobin ⁵, Meghan McGuire ⁵, Rhoda W Y Law ⁵, Andrew J Elia ⁵, Ben X Wang ⁵, Ian King ^8,⁹, Tong Zhang ⁸, Trevor J Pugh ^5,^10,¹¹, Zaid Saeed Kamil ^5,⁹, Marcus Butler ^5,¹², Frances A Shepherd ^5,¹², Natasha B Leighl ^5,¹², Albiruni Abdul Razak ^5,¹², Aaron Hansen ^5,¹², Samuel D Saibil ^5,¹², Philippe L Bedard ^5,¹², Peter M Siegel ^4,⁶, Lillian L Siu ^5,¹², David W Cescon ^5,¹², Anna Spreafico ^5,^12,^✉

PMCID: PMC12873207 PMID: 41484121

Abstract

Class 2 and 3 non-V600E BRAF mutations are oncogenic drivers in many cancer types. Currently, there are no established targeted therapies with proven efficacy for cancers with non-V600E BRAF mutations. We developed the investigator-initiated, Phase II BEAVER clinical trial (NCT03839342) to evaluate the efficacy of BRAF and MEK inhibitors in patients with non-V600E BRAF mutations. The primary outcome was objective response rate (ORR). The best ORR was 14% (3/21), the primary endpoint was not met. By analyzing genomic data from patient tumors, circulating tumor DNA (ctDNA), patient-derived xenograft (PDX) models generated from enrolled patients, and Class 2 & 3 BRAF mutant cell lines, we discovered MAPK-dependent and independent mechanisms of resistance to BRAF/MEK inhibition. These mechanisms include the acquisition of new mutations in NRAS, MAP2K1, RAF1, and RB in ctDNA at the time of disease progression. CDK4/6 and SHP2 emerge as mediators of intrinsic resistance to BRAF/MEK inhibition in Class 2 & 3 BRAF mutant tumors. Therapeutic strategies combining CDK4/6 or SHP2 inhibitors with BRAF/MEK inhibitors in preclinical models show greater efficacy than BRAF/MEK inhibitors alone in these cancers.

Subject terms: Cancer therapeutic resistance, Drug development

There is a lack of effective therapies for patients with non-V600E BRAF mutant cancer. Here, the authors report limited response in a phase II trial investigating the combination of binimetinib (MEK inhibitor) and encorafenib (BRAF inhibitor) for the treatment of non-V600E BRAF mutant cancer and subsequently investigate resistance mechanisms and combination therapeutic strategies in patient-derived models.

Introduction

BRAF is one of the most frequently mutated actionable oncogenes in cancer^1,2. Oncogenic Class 1 (V600) mutations in BRAF render this kinase constitutively active, leading to excessive Mitogen Activated Protein Kinase (MAPK) pathway activity, increased cell proliferation, and tumorigenesis. Combined inhibition of BRAF and its downstream kinase, MEK, is an effective therapeutic strategy for most cancer types with BRAF V600E mutations³. However, non-V600 BRAF mutants account for approximately 30% of all oncogenic BRAF mutations in adult solid tumors⁴, and currently there are no established targeted therapies for non-V600 BRAF mutant tumors.

There are a wide array of oncogenic non-V600 BRAF mutations that can be subdivided into Class 2 and 3 mutations¹. Unlike Class 1 BRAF mutants—which signal as monomers—Class 2 and 3 non-V600 BRAF mutants signal as dimers. Class 2 BRAF mutants form RAS-independent dimers with intermediate-to-high kinase activity⁵, whereas Class 3 BRAF mutations confer low kinase activity but form RAS-dependent dimers with other RAF proteins to hyperactivate the downstream MEK and ERK kinases⁶. Indeed, RAS mutations frequently co-occur with Class 3 BRAF mutations in cancer^4,6. In RAS wild-type tumors, the protein tyrosine phosphatase, SHP2, has been implicated as a key regulator of RAS activity that potentiates RAF dimerization^7,8. Once phosphorylated, ERK regulates cell cycle progression by inducing the transcription of Cyclin D1, promoting its assembly with the cyclin-dependent kinases, CDK4 and CDK6⁹. Cyclin-D-bound CDK4/6 and Cyclin-E-bound CDK2 kinases phosphorylate and inactivate the tumor suppressor RB¹⁰. This step is essential for cells to progress through the G1-S phase of the cell cycle. Excessive cell-cycle progression is prevented in part by the tumor suppressor, p21, a p53-regulated cyclin-dependent kinase inhibitor protein, which inhibits the activity of CDK2 and in some contexts CDK4/6¹¹. Therefore, in cells with loss-of-function TP53 mutations, progression through the cell cycle is a less tightly controlled process. Of note, TP53 mutations are also more likely to co-occur in tumors with Class 2 and 3 vs. Class 1 BRAF mutations⁴.

RAS mutations can potentiate BRAF inhibitor-induced paradoxical activation of the MAPK pathway in BRAF wild-type tumors¹²; thus, earlier studies proposed MEK inhibitor monotherapy for tumors with Class 2 and 3 BRAF mutations¹³. However, one prospective clinical trial demonstrated a lack of clinical efficacy for trametinib monotherapy in these tumors¹⁴. Subsequently, it was reported that BRAF monomer inhibitors do not promote paradoxical activation of the MAPK pathway even in RAS co-mutated non-V600E BRAF mutant tumors¹⁵. Moreover, combined BRAF and MEK inhibition was more effective than MEK inhibitor monotherapy at inhibiting tumor growth in preclinical models of cancers with Class 2 or 3 BRAF mutations^15,16. A recent systematic review of primarily retrospective data revealed response rates as high as 50% in patients with Class 2 or 3 BRAF mutant solid tumors that were treated with combined BRAF + MEK inhibitors¹⁷.

In this work, we describe the results of a Phase II clinical trial that was designed to prospectively evaluate the efficacy of Binimetinib (MEK inhibitor) and Encorafenib (BRAF inhibitor) for the treatment of Advanced solid tumors with non-V600E BRAF mutations (BEAVER trial). We also characterize molecular mechanisms associated with treatment response and resistance and evaluated alternative therapeutic strategies that are designed to overcome resistance to binimetinib and encorafenib.

Results

Clinical characteristics and outcomes of patients enrolled on the BEAVER trial

The BEAVER trial was a Princess Margaret Cancer Centre investigator-initiated, Phase II clinical trial evaluating binimetinib and encorafenib (B + E) for the treatment of advanced solid tumors with non-V600E BRAF mutations, with a primary objective of overall response rate (ORR) by RECIST v1.1. Patients were recruited from July 2019 to February 2023. Exploratory objectives included genomic and transcriptomic profiling to interrogate mechanisms of treatment response and resistance and development of patient-derived xenograft (PDX) models of non-V600E BRAF mutant cancers (Fig. 1a). Twenty-seven patients were screened and 23 out of a planned 26 patients were enrolled in the BEAVER trial. The trial closed early due to poor accrual during the COVID-19 pandemic. Enrolled patients’ characteristics are described in (Supplementary Table 1). The tumor types included melanoma, colorectal (n = 6 each), biliary tract (n = 5), lung (n = 2), and breast, pancreas, uterine, and small bowel cancers (n = 1 each). The median age was 59 years. Patients’ tumors had Class 1 (n = 1), Class 2 (n = 9), and Class 3 (n = 13) BRAF mutations.

Twenty-one patients were evaluable for ORR. The best ORR was 14% (3/21) (Fig. 1b). One patient with a Class 3 (BRAF D594G) ampullary cancer had a confirmed partial response (PR) and two melanoma patients with Class 2 BRAF mutations (K601E, G469S) experienced unconfirmed PRs. Four patients had stable disease (SD) as best response, two patients were non-evaluable (NE) for response, and 14 patients had progressive disease (PD) as best response. The predefined primary endpoint was not met. In the entire cohort, the median PFS was 2.3 months and the median OS was 6.1 months (Supp. Fig. 1). This study did not meet the pre-specified criteria of 4/26 responses required for further investigation of this regimen in this patient population. Twenty-three patients were evaluable for safety. The adverse event profile of B + E (Supplementary Table 2) was similar to what has previously been reported for this regimen^18,19. In this cohort, 22% of patients experienced Grade 3 treatment related adverse events. There were no Grade 4 or 5 treatment related adverse events and no new safety signals were identified.

We performed secondary analyses to investigate clinical, pathologic and genomic biomarkers of response and resistance. The ORR and PFS did not differ significantly according to BRAF mutation Class (Supp. Fig. 2). However, cancer type was associated with differences in response rate and PFS (Supp. Fig. 3a–c). Patients with melanoma (4.0 months) and biliary tract tumors (4.5 months) experienced longer PFS than patients with colorectal or other tumor types (1.8 months; P = 0.016). We observed a significant decrease in MAPK activity (pERK IHC) in on-treatment biopsies compared to pre-treatment baseline biopsies (Supp. Fig. 3d), confirming the expected on-target effects of B + E.

Genomic characteristics of patients’ tumors

Targeted panel sequencing of relevant cancer driver genes was performed on archival tumors or fresh pre-treatment tumor biopsies for all patients enrolled on the BEAVER trial. We confirmed the presence of an oncogenic BRAF mutation in 20/23 (87%) cases, and co-occurring pathogenic variants were identified in 21/23 (91%) tumors (Fig. 1c). Pathway analysis of the co-altered genes revealed enrichment for alterations in multiple potentially actionable pathways (Fig. 1d). In this cohort 12/23 (52%) patients had a co-occurring MAPK pathway activating mutation in: NF1 (n = 4), KRAS (n = 3), NRAS (n = 2), RAF1 (n = 2) or MAP2K1 (n = 1) genes at baseline. The most frequently co-altered gene was TP53, which was mutated in 9/23 (39%) tumors. All evaluable patients with tumor TP53 mutations had PD as best response (Fig. 1c). Patients with melanoma or biliary tract tumors that were TP53 wild-type were significantly more likely to have SD or PR as best response (Supp. Fig. 3e) and experienced longer PFS (4.4 months) compared to patients who had other cancer types or patients with melanoma/biliary tract cancer with TP53 mutations (1.2–1.6 months; P = 0.004) (Fig. 1e).

Development of pre-clinical models of BRAF/MEK inhibitor resistant Class 2 & 3 BRAF mutant cancers

The response rate to B + E treatment in this population of patients with non-V600E BRAF mutations was substantially lower, and the PFS was shorter, than it is for patients with BRAF V600 mutations^18,19. Therefore, we sought to identify mechanisms of resistance to B + E in cancers with Class 2 and 3 BRAF mutations. To do this, we developed patient-derived xenograft (PDX) models from patients enrolled on the BEAVER trial. Fresh tumor biopsy samples from 13 patients were implanted into NSG mice. The details of patients, biopsies and BRAF mutations are described in (Supplementary Table 3). Of these, PDXs were successfully established from 9/13 (69%) patient samples (Fig. 2a). Successful engraftment of a PDX was associated with an increased % change in target lesion size by RECIST 1.1 in the corresponding patient (Supp. Fig. 4a). We performed whole exome sequencing on PDXs and confirmed the presence of a non-V600E BRAF mutation in 8/9 established PDXs (Supp Fig. 4b). Amongst the 8 BRAF mutant PDXs, there was a high concordance of oncogenic alterations between the patient tumor and the established PDX (Supp. Fig. 4b). We evaluated the ability of B + E to promote tumor growth inhibition in the non-V600E BRAF mutant PDXs in vivo (Supp. Fig. 4c). We observed a significant indirect correlation between the amount of B + E induced tumor growth inhibition in PDXs in vivo with the change in target lesion size from the corresponding patients (Supp. Fig. 4d). To expand our panel of preclinical models of B + E-resistant non-V600E BRAF cancers, we separately generated four independent Class 2 BRAF mutant cancer cell lines with acquired resistance to B + E (Supp. Fig. 5a). These included two melanoma (HMV-II, FM95), one breast cancer (MDA-MB-231), and one prostate cancer (22RV1) cell line. Cells were grown continuously in the presence of B + E—in a 1:5 ratio of binimetinib:encorafenib, to be consistent with the clinical drug dosing ratio—for several months. B + E resistance was confirmed by clonogenic assay (Supp. Fig. 5b) and by comparing the IC50 of all parental/resistant pairs (Supp. Fig. 5c and Fig. 2b). B + E induced apoptosis in all Class 2 parental cell lines, and there was a significant reduction in B + E-induced apoptosis in the B + E-resistant melanoma cells. However, there was no significant difference in apoptosis levels between B + E-treated MDA-MB-231 and 22RV1 parental/resistant cells (Supp. Fig. 5d). Together, these models allowed us to study the intrinsic and adaptive mechanisms of resistance to B + E in Class 2 and 3 BRAF mutant cancers.

Fig. 2 — A Tumor growth curves of P0 PDXs derived from patients enrolled on the BEAVER trial. B Bini+Enco IC50 values for each parental/resistant pair, N = 3 biological replicates plotted (MDA-MB-231 & 22RV1), N = 4 biological replicates plotted for HMV-II & FM95. IC50s that were not achieved were represented as the maximal concentration tested (2000 nM Bini + 10000 nM Enco). Unpaired t-test. Data are presented as mean values +/− SD. C Representative immunoblots of MAPK pathway activity in the Class 2 BRAF mutant parental/resistant cancer cell lines. Cells were treated with Bini + Enco (100 nM Bini + 500 nM Enco) for 2, 24, 48, and 72 h or DMSO (B + E 0) for 72 h. Similar data was obtained N = 3 independent times. D Quantified GTP-bound RAS over total RAS for each cell line. Cells were treated with DMSO or Bini + Enco (100 nM Bini + 500 nM Enco) for 24 h. N = 3 (HMV-II, FM95, & 22RV1) and N = 4 (MDA-MB-231) biological replicates plotted. Line on data points represents the median. One-way ANOVA with Šídák’s correction for multiple comparison. E Representative western blots of the BRAF co-immunoprecipitation in the 4 BRAF Class 2 parental and resistant cell lines. Cells were treated with DMSO or Bini + Enco (100 nM Bini + 500 nM Enco) for 24 h. Similar data was obtained N = 3 (HMV-II) or N = 4 (FM95, MDA-MB-231, & 22RV1) independent times. F Representative images of HMV-II CRISPR-Cas9 parental cells at passages 0 vs. 3 treated with either DMSO or Bini + Enco (5 nM Bini + 25 nM Enco). Similar data was obtained N = 4 independent times. Scale bar: 100 μm. G Relative fitness of HMV-II CRISPR-Cas9 parental cells with *NRAS* or NT knockdown under DMSO or Bini + Enco (5 nM Bini + 25 nM Enco) treatment. N = 4 biological replicates plotted. Data plotted as mean +/− SD. Two-way ANOVA was performed on AUC values with Dunnett correction for multiple comparisons for gRNA NT vs. gRNA1-*NRAS* and gRNA2-*NRAS* (N = 4, p < 0.0001), and DMSO vs. B + E (p < 0.0001). H ctDNA results analysis from BVR-M-05 patient on the BEAVER trial. Plasma was collected and ctDNA was analyzed using the TSO500 ctDNA v2 assay. VAF variant allele frequency, C1D1 cycle 1, day 1. Source data is located in the Source Data file.

Characterization of cell cycle alterations in BRAF/MEK inhibitor resistant Class 2 BRAF mutant cells

MAPK pathway reactivation is a common feature of BRAF/MEK inhibitor resistance in melanomas with Class 1 BRAF mutations^20,21. We sought to determine if this phenomenon is recapitulated in Class 2 BRAF mutant cells. Therefore, we assessed the relative degree of MAPK pathway activity (via ERK phosphorylation, pERK) in parental and resistant Class 2 cells. The Class 2 BRAF mutant resistant melanoma cells lines (HMV-II, FM95) displayed robust MAPK re-activation, despite treatment with B + E (Fig. 2c and Supp. Fig. 5e). However, MAPK pathway inhibition was minimal in parental MDA-MB-231 breast cancer cells and remained unchanged in resistant cells. Conversely, B + E potently inhibited MAPK pathway activity in both parental and resistant 22RV1 prostate cancer cells (Fig. 2c and Supp. Fig. 5e). These results suggest that Class 2 BRAF mutant cancers do not universally employ MAPK pathway reactivation as a resistance mechanism to BRAF/MEK inhibition.

RAS mutations commonly co-occur with Class 2 BRAF mutations in patients’ tumors, and acquired RAS mutation are an established mechanism of resistance to BRAF/MEK inhibitors in BRAF V600 mutant cancers²². Thus, we investigated RAS activity levels with RAS-GTP assays in our Class 2 BRAF mutant cell lines. As expected, RAS activity was substantially higher in RAS mutant cells (HMV-II and MDA-MB-231) compared to the RAS WT cells (Supp. Fig. 6a). We compared RAS activity levels across parental and resistant cells treated with DMSO or B + E. There was no change in RAS activity levels in 3/4 cell lines (HMV-II, MDA-MB-231, and 22RV1). However, there was a significant increase in RAS activity in RAS wildtype FM95 resistant B + E treated cells (Fig. 2d and Supp. Fig. 6b). Class 2 BRAF mutants are dimerization dependent and RAS activity promotes RAF dimerization; therefore, we asked whether the resistant cells formed increased BRAF:CRAF dimers compared to the parental cells. Indeed, we observed a significant increase in BRAF:CRAF dimers in B + E treated FM95 resistant cells (Fig. 2e and Supp. Fig. 6c). In RAS mutant HMV-II and MDA-MB-231 cells, B + E potentiated BRAF:CRAF co-immunoprecipitation in both parental and resistant cells. We did not observe significant changes in BRAF:CRAF co-immunoprecipitation in 22RV1 cells. Taken together, these results show that in conditions where RAS activity is high (HMV-II & MDA-MB-231 parental and resistant, and FM95 resistant cells), B + E promotes BRAF:CRAF co-immunoprecipitation, thus contributing to MAPK-reactivation.

Next, we sought to determine whether mutant RAS was necessary for B + E resistance in Class 2 BRAF mutant cancers. We performed CRISPR competition assays to evaluate whether RAS knockout impacted the relative fitness of cells to proliferate in the presence of B + E. In Class 2 BRAF G469V, NRAS Q61K, mutant HMV-II cells, NRAS knockout led to a significant reduction in the relative cell fitness compared to NT cells in DMSO. Cell fitness was further compromised under B + E treatment, demonstrating that mutant NRAS drives intrinsic resistance to B + E in HMV-II cells (Fig. 2f, g). Intriguingly, when we analyzed serial samples of ctDNA collected from a patient with Class 2 BRAF mutant melanoma (BVR-M-05) who initially experienced a response but quickly developed resistance, we identified an acquired NRAS mutation that was detectable only at the time of progression (Fig. 2h). These findings substantiate the clinical relevance of mutant NRAS as a driver of MAPKi resistance in Class 2 BRAF mutant melanoma. In Class 2 BRAF G464V, KRAS G13D mutant MDA-MB-231 cells subjected to KRAS knockout, we also observed that KRAS was essential for cell proliferation, however, this effect was not further enhanced in the presence of B + E (Supp. Fig. 6d, e). Conversely, 22RV1 cells do not show MAPK-dependent mechanisms of resistance to B + E. Given this is a prostate cancer model with an activating AR mutation (AR H875Y), we hypothesized that androgen receptor (AR) signaling pathway would be essential for growth in this model. Indeed, we found that AR was essential for the growth of 22RV1 cells in the presence or absence of B + E (Supp. Fig. 6f, g). Thus, we confirmed that Class 2 BRAF mutant cancers can employ MAPK-dependent and MAPK-independent mechanisms to promote cell proliferation in the presence of B + E.

We identified various non-overlapping drivers of resistance in our Class 2 BRAF mutant models. To identify potentially actionable downstream mediators of resistance that are shared across multiple Class 2 models, we performed transcriptomic analyses of these models. The four pairs of parental/resistant cells were treated for 24 h in the presence or absence of B + E and subjected to RNA sequencing (RNA-Seq) and analysis. In principal component analyses, the primary differential component was determined by cancer type rather than cell line resistance to BRAF/MEK inhibitors (Fig. 3a). Gene Set Enrichment Analysis (GSEA) revealed 8 gene sets that were commonly enriched in at least 3/4 B + E-treated resistant cells compared to B + E-treated parental cell lines (Fig. 3b). This included two gene-sets (E2F targets & G2M Checkpoint) that were altered in the BEAVER trial patient tumors (Fig. 1d) and responsible for mediating cell cycle progression. Additionally, B + E treatment potently suppressed expression of genes in the E2F Targets and G2M Checkpoint gene sets in Class 2 parental cells, but this effect was abrogated in resistant cells (Fig. 3c, d and Supp. Fig. 7a). Drug-induced MAPK pathway inhibition was confirmed by evaluating the MAPK pathway activity score (MPAS²³, Supp. Fig. 7a, b) and was observed in all Class 2 cell lines and did not differ by cancer type (Supp. Fig. 7c). However, B + E did more potently suppress expression of the E2F/G2M gene sets in melanoma vs. non-melanoma cell lines (Supp. Fig. 7c). Thus, we hypothesized that cell cycle progression may be altered as a resistance mechanism in our B + E-resistant cells.

Fig. 3 — A Principal Component Analysis plot of the Bini + Enco and DMSO treated Parental/Resistant non-V600 BRAF Class 2 cell lines. B Negative log10 of the p value from GSEA comparing each Bini + Enco treated Parental/Resistant non-V600 BRAF Class 2 cell lines. Plotted Gene Sets from the top 15 most enriched common gene sets between the 4 cell lines. C Heatmap of the Hallmark MSigDB E2F Targets gene set (genes = 198, baseMean > 0) in the Bini+Enco and DMSO treated Parental/Resistant non-V600 BRAF Class 2 cell lines. D Heatmap of the Hallmark MSigDB G2M Checkpoint gene set (genes = 196, baseMean > 0) in the Bini + Enco and DMSO treated Parental/Resistant non-V600 BRAF Class 2 cell lines. E Immunoblot of cell cycle proteins from synchronized cells treated with DMSO or Bini (100 nM) + Enco (500 nM) for FM95 and 22RV1 and Bini (200 nM) + Enco (1000 nM) for HMV-II and MDA-MB-231 for 48 h. Similar data was obtained N = 3 independent times. F Bar graph of the cell cycle phase distribution based on propidium iodide flow cytometry cell cycle analysis. Cells were synchronized overnight and treated with DMSO or Bini (200 nM) + Enco (1000 nM) for 24 h. Differences were evaluated with a 2-way ANOVA with Tukey correction for multiple comparisons. Technical replicates were used to compute statistical analyses for cell cycle experiment as variation in cell cycle phases may exist between different cell populations even from daughter cells. Data is presented as mean values +/− SD, N = 6 technical replicates plotted. G Responses to Bini+Enco treatment amongst BEAVER trial patients, stratified according to the presence or absence of oncogenic alterations in genes within the E2F Targets or G2M Checkpoint gene sets (P = 0.023, Fisher’s exact test, two-tailed, unadjusted). H MDA-MB-231 cells were transfected with the indicated concentrations of siRNA and analyzed by immunoblotting at experimental endpoint. Similar data was obtained N = 3 independent times. I Proliferation assay comparing the response of siRNA-transfected MDA-MB-231 cells to either low (Bini 25 nM + Enco 125 nM) or high dose (Bini 200 nM + Enco 1000 nM) drug treatment. Two-way ANOVA, Tukey correction. Data is presented as mean values +/− SD, N = 3 biological replicates plotted. Source data is located in the Source Data file.

Cell cycle progression is tightly regulated by the tumor suppressor RB. RB is phosphorylated by cyclin-dependent kinases, CDK2, CDK4, and CDK6, liberating E2F to promote transcription of E2F target genes required for G1-S phase transition. CDK activity is regulated by cyclin D (CDK4/6) and cyclin E (CDK2). Therefore, we asked whether resistant cells were characterized by changes in these proteins. In synchronized cells, a 48 h B + E treatment potently suppressed cyclin D protein and/or phosphorylated RB (pRB) levels in parental Class 2 BRAF mutant melanoma cells, to a lesser extent in MDA-MB-231 cells, but did not suppress cyclin D/pRB in 22RV1 cells (Fig. 3e). In resistant cells, B + E treatment was not sufficient to inhibit RB phosphorylation. We did not observe any substantial or consistent differences in CDK protein expression level between parental and resistant cell lines (Fig. 3e). Next, we assessed cell cycle distribution in parental and resistant cells. B + E treatment significantly increased G0/G1 arrest in all parental Class 2 cells and this B + E-dependent effect was abrogated in all resistant cells (Fig. 3f). B + E reduced the percentage of cells in S and/or G2/M-phase in all parental cell lines, but this effect was diminished in resistant cells (Fig. 3f and Supp. Fig. 7d). Moreover, BEAVER trial patients with tumors harboring co-alterations in genes within the E2F Targets (CCNE1, PMS2, BRCA2, POLE, and TP53) and/or G2M Checkpoints (SMAD3, BRCA2, and POLE and TP53) gene sets were intrinsically resistant to B + E treatment (Fig. 3g). These data highlight the potential therapeutic value of targeting mediators of cell cycle progression to enhance the efficacy of B + E in Class 2 BRAF mutant cancers.

Evaluating BRAF/MEK/CDK4/6 inhibitor combinations in Class 2 BRAF mutant cancers

CDK4/6 kinases are critical in regulating G1-S-phase transition and represent actionable therapeutic targets in cancer. Therefore, we tested whether CDK4 and/or CDK6 are required to mediate resistance to B + E in Class 2 BRAF mutant cancer cells. In MDA-MB-231 cells, CDK4 and CDK6 were knocked down by siRNA alone or in combination (Fig. 3h). Knockdown of either CDK4 or CDK6 alone did not inhibit cellular proliferation or potentiate the growth inhibition effect mediated by B + E. However, simultaneous knockdown of CDK4 and CDK6 inhibited the proliferation of DMSO treated cells. Notably, the combined knockdown further potentiated cell growth inhibition mediated by B + E at high doses (Fig. 3i). Together, these data led us to investigate the therapeutic benefit of a combined CDK4/6 inhibition with BRAF and MEK inhibition in Class 2 BRAF mutant cancers.

We assessed the efficacy of the CDK4/6 inhibitor, palbociclib, for inhibiting cell proliferation of Class 2 BRAF mutant parental cancer cells, when used alone or in combination with B + E. In line with our CDK4/6 knockdown experiments, treatment with palbociclib alone inhibited cell survival in 3/4 Class 2 BRAF mutant cell lines. The triple therapy combination (B + E+palbociclib; B + E + P) was more effective at inhibiting cell growth vs. B + E in all four Class 2 cell lines tested (MDA-MB-231, FM95, 22RV1, H2087) (Fig. 4a–d). Next, we investigated the effect of this combination on tumor growth in vivo in a Class 2 BRAF mutant PDX colorectal cancer model (BVR-O-17), and two additional Class 2 BRAF mutant melanoma PDX models (GCRC-Mel1, GCRC-2015). B + E + P significantly inhibited tumor growth in these Class 2 BRAF mutant PDX models compared to vehicle treatment (Fig. 4e–g). We observed inhibition of the MAPK pathway via ERK phosphorylation in all 3 PDX models treated with B + E. Palbociclib alone did not inhibit ERK phosphorylation but effectively inhibited RB phosphorylation. B + E + P inhibited both ERK and RB phosphorylation in the 3 models, though some phosphorylation of these proteins remained in certain tumors (Fig. 4h–j). Similar results were observed in Class 3 BRAF mutant models (Supp. Fig. 8a–d). We did not observe any increased toxicity with B + E + P compared to B + E, as evidence by no reduction in animals’ weights (Supp. Fig. 8e). To further investigate the impact of B + E + P triple therapy on transcriptional outputs, we calculated the MAPK pathway activity score (MPAS)²³ along with the E2F and G2M gene set scores from the RNA sequencing of these PDXs taken at the experimental end-point. B + E treatment significantly inhibited downstream MAPK pathway activity compared to vehicle-treated tumors; however, B + E did not impact transcript levels of E2F targets or G2M checkpoint gene sets. Conversely, treatment with palbociclib alone inhibited expression of E2F targets and G2M checkpoints gene sets but did not impact MAPK pathway activity. However, B + E + P triple therapy significantly repressed all 3 gene sets (MPAS, E2F targets, and G2M checkpoint) compared to vehicle treatment in multiple PDX models (p < 0.0001) (Fig. 4k–m and Supp. Fig. 9). These data reinforce the potential therapeutic benefit of combined MAPK pathway and CDK4/6 inhibition for the treatment of non-V600E BRAF mutant tumors.

Fig. 4 — Quantification of clonogenic assays of cancer cells with endogenous Class 2 BRAF mutations that were treated with combinations of BRAF, MEK, and CDK4/6 inhibitors, encorafenib, binimetinib and palbociclib respectively in A MDA-MB-231 breast cancer (1000 nM Enco, 200 nM Bini, 50 nM Palbo). B FM95 melanoma (150 nM Enco, 30 nM Bini, 100 nM Palbo). C 22RV1 prostate cancer (25 nM Enco, 5 nM Bini, 25 nM Palbo) and D H2087 NSCLC cells (125 nM Enco, 25 nM, 100 nM Palbo). One-way ANOVA with Šídák’s multiple comparisons test. Data presented as mean values +/− SEM, N = 3 biological replicates (22RV1, H2087, MDA-MB-231 & FM95). Tumor growth curves of E GCRC-Mel1, F GCRC-2015, and G BVR-O-17 PDXs treated with vehicle, palbociclib (80 mg/kg/day) (palbo) for all PDXs, binimetinib (15 mg/kg/d) + encorafenib (75 mg/kg/d) (bini + enco) for GCRC-Mel1 and BVR-O-17, and binimetinib (20 mg/kg/d) + encorafenib (75 mg/kg/d) (bini + enco) for GCRC-2015. For the triple therapy of binimetinib + encorafenib + palbociclib (B + E + P), the doses of the respective drugs were 15/75/80 mg/kg/d for GCRC-Mel1, 20/75/80 mg/kg/d for GCRC2015, and 15/50/80 mg/kg/d for BVR-O-17. One vehicle-treated mouse (BVR-O-17) reached a humane end-point on Day 14. Data are presented as mean values +/− SEM. One-way ANOVA on AUC with Tukey correction for multiple comparisons. Corresponding immunoblots of tumors taken at experimental endpoint from mice bearing PDXs H GCRC-Mel1, I GCRC-2015, and J BVR-O-17 that were treated with vehicle, bini+enco, palbociclib, or bini + enco + palbo. Each lane represents protein lysate from a different biological replicate (tumor). Graphs plotting RNA sequencing Z-scores for comparing the K MPAS score (10 genes), L E2F Targets score (198 genes), and M G2M Checkpoint score (190 genes) from BVR-O-04 (circle), BVR-O-17 (triangle), and GCRC-Mel1 (square) treated with Vehicle, Bini+Enco, Palbo, or Bini + Enco + Palbo. The box plot indicates the 25–75th percentiles (box boundary), median values (line in box), and min/max values (whiskers). Ordinary One-Way ANOVA with Tukey’s multiple comparison testing. Source data is located in the Source Data file.

Identifying SHP2 as a therapeutic target in BRAF/MEK inhibitor resistant Class 3 tumors

We sought to identify potentially actionable genes that are essential for the growth of Class 3 BRAF mutant tumors. To do this, we first mined the DepMap portal^24,25 to define and compare the essential genes for growth of Class 3 (n = 7) and Class 1 (n = 117) BRAF mutant cancer cell lines (Fig. 5a). We were interested in identifying genes that may be implicated in intrinsic B + E resistance in Class 3 BRAF mutant cancers. Multiple genes that were more essential in Class 3 cancer cells encoded proteins that were also constitutively activated in Class 3 BRAF mutant tumors from patients enrolled on the BEAVER trial. For example, 4/13 (31%) of patients with Class 3 BRAF mutations had co-occurring activating mutations in RAF1 or NRAS (Fig. 1c). We next investigated whether any of these essential genes were associated with acquired resistance to B + E in patients with Class 3 BRAF mutant cancer. We used the TSO500 targeted hybrid capture based next generation sequencing assay to quantify the mutations present in the ctDNA from two patients with Class 3 BRAF mutant cancer. Both patients initially experienced tumor regression with B + E treatment, but ultimately went on to develop disease progression (Fig. 5b). Both patients had detectable ctDNA at baseline, but cleared their ctDNA after 1 cycle of B + E treatment. At the time of disease progression, one patient (BVR-O-07) developed a new RAF1 mutation (p.R391W) that confers high kinase activity in a dimerization-dependent manner^26,27, and two new MAP2K1 mutations that are both RAF-regulated, meaning that these MAPK2K1 mutations require upstream RAF activity to function as oncogenes²⁸. A second patient (BVR-M-08) developed five new oncogenic NRAS mutations—each at different allele frequencies, suggestive of multiple subclones. We did not observe any new acquired MAPK mutations in patients with Class 3 BRAF mutant cancer who experienced PD as best response, although one patient (BVR-O-13) did acquire a new loss of function mutation in the RB tumor suppressor at the time of PD (Supp. Fig. 10). In contrast, no new MAPK-activating mutations were identified amongst patients with Class 3 BRAF mutations who experienced PD as best response. Altogether, these somatic activating mutations in the MAPK pathway suggest a MAPK/ERK pathway-dependent mode of resistance that requires RAF-dimerization, which is a SHP2/RAS-dependent process.

To identify additional genes that may be required for B + E-resistance, we investigated the transcriptomes of Class 3 BRAF mutant PDXs (n = 5). Of these five PDXs, two were sensitive and three were intrinsically resistant to B + E (Supp. Fig. 4c, d). Principal component analysis of the PDX tumors revealed that the B + E sensitive tumors clustered together and away from the B + E-resistant tumors (Fig. 5c). There were 3651 significantly differentially expressed genes between B + E-resistant vs. sensitive tumors (Supp. Fig. 11a, b). The resistant tumors were significantly enriched for 3 gene sets, two of which were related to KRAS Signaling (Fig. 5d). The enrichment of these gene sets supports the notion that RAS activation is involved in mediating resistance to B + E in the Class 3 BRAF mutant PDXs. Indeed, patients with activating KRAS/NRAS mutations experienced more tumor growth (relative increase in the sum of target lesion diameters) with B + E treatment compared to patients without RAS activating mutations at baseline (Fig. 5e). Of the 1469 overexpressed genes in resistant vs. sensitive Class 3 PDXs, 28 were included in the list of essential genes for Class 3 BRAF mutant cancers (Fig. 5f). Amongst these genes, PTPN11 was one of the most essential genes in Class 3 cancer cell lines (Fig. 5g) that was also more highly expressed in B + E-resistant Class 3 PDXs compared to B + E-sensitive PDXs (Fig. 5h). PTPN11 encodes the nonreceptor protein tyrosine phosphatase, SHP2. SHP2 activates the MAPK and PI3K pathways²⁹ through forming a complex with GRB2, GAB1, and SOS1 to activate the RAS superfamily of small GTPases^30,31. In addition, SHP2 cooperates with several other proteins (EGFR, NRAS, CRAF) that were also found to be essential for the proliferation of Class 3 cancer cells (Fig. 5a). In our analysis, we found that the two outlier Class 3 BRAF mutant cell lines with lower PTPN11 gene essentiality scores (Fig. 5g) harbor activating RAS mutations, hence the dispensability of PTPN11 in these cell lines.

To confirm that PTPN11 is an essential gene in Class 3 BRAF mutant cells, we performed a two-color CRISPR competition assay (Fig. 5i, j). NCI-H1666 lung cancer cells stably expressing Cas9 were engineered to express either GFP in addition to a PTPN11-targeting gRNA, or mCherry in addition to a gRNA targeting the adeno-associated virus integration site (AAVS1) and were co-cultured together. Cells expressing 2 different PTPN11, but not non-targeting (NT), guide RNAs (gRNAs) were outcompeted by the AAVS1 gRNA-expressing cells (i.e., wildtype for PTPN11), hence validating the predicted PTPN11 gene essentiality for cell growth. Next, we assessed whether small molecule SHP2 inhibitors (SHP099, TNO155) could inhibit the growth of BRAF mutant cells in vitro (Fig. 5k-l). As reported previously^7,32, we found that SHP2 inhibitors did not inhibit proliferation of Class 1 and 2 BRAF mutant cell lines. In contrast, while Class 3 BRAF mutant cells were sensitive to SHP2 inhibition, those with co-occurring RAS mutations (2 melanoma cell lines, WM3670, WM3629) were also insensitive to SHP2 inhibitor monotherapy. Together, these results highlight SHP2 as a potential mediator of intrinsic and acquired resistance to B + E and a promising therapeutic target in Class 3 BRAF mutant cancers.

Evaluating BRAF/MEK/SHP2 inhibitor combinations in Class 3 BRAF mutant cancers

We assessed whether the SHP2 inhibitor, TNO155, enhanced MAPK inhibitor-induced cell growth inhibition of Class 3 BRAF mutant cancer cells in vitro. The triple therapy combination (B + E + TNO155; B + E + T) was more effective at inhibiting cellular proliferation and inhibiting MAPK pathway activity vs. B + E and TNO (low dose of 100 nM) in four Class 3 cell lines (H508, H1666, HT55, WM3670) (Fig. 6a) and this corresponded with enhanced MAPK pathway inhibition by immunoblot in H508 and H1666 cells (Fig. 6b). We did not observe any additional effect of adding SHP2 inhibitors to B + E in Class 2 BRAF mutant cancer cell lines (Supp. Fig. 12a, b). Similarly, we found that the triple therapy was more effective at inhibiting growth of two 3D spheroid models of Class 3 BRAF mutant melanoma (WM3629, WM3670) vs. B + E and/or TNO (Supp. Fig. 12c–e). Next, we investigated the effect of this drug combination on tumor growth in vivo using several Class 3 BRAF mutant PDX models. B + E + T significantly inhibited tumor growth or induced tumor regression in all PDX models tested, including those that were intrinsically resistant to B + E (BVR-O-12; BVR-O-15) (Fig. 6c–f and Supp. Fig. 12f). However, this increased efficacy was also associated with increased weight loss with the B + E + T combination (Supp. Fig. 12g). While we observed varying degrees of MAPK pathway inhibition in PDX models treated with TNO155 alone, B + E + T treatment consistently inhibited MAPK pathway (pERK) in all PDX models evaluated (Fig. 6g–i). Similarly, we calculated the MPAS and found that B + E + T treatment significantly inhibited downstream MAPK pathway activity compared to vehicle-treated tumors, more so than those treated with B + E treatment (Fig. 6j). Altogether, these data support the potential benefit of using Shp2 inhibitors for the treatment of Class 3 BRAF mutant tumors to overcome resistance to BRAF/MEK inhibition.

Fig. 6 — A Quantification of clonogenic assays of cancer cells with endogenous Class 3 *BRAF* mutations that were treated with combinations of BRAF, MEK, and SHP2 inhibitors, encorafenib, binimetinib, and TNO155, respectively in H508 (colorectal), H1666 (NSCLC), HT55 (colorectal), and WM3670 (melanoma) cells. Drug doses: H508 and HT55 - Bini 80 nM, Enco 400 nM, TNO 100 nM and 1 µM; H1666 - Bini 40 nM, Enco 200 nM, TNO 100 nM and 1 µM; WM3670 - Bini 120 nM, Enco 600 nM, 100 nM and 1 µM. N = 3 biological replicates plotted. Data are presented as mean values +/− SEM. One-way ANOVA with Šídák’s correction for multiple comparisons. B Corresponding immunoblots of conditions from (A) after 2 h treatment (representative of N = 3 biological replicates). Tumor growth curves of C BVR-O-10, D BVR-O-12, and E BVR-O-13 PDXs treated with Vehicle, Binimetinib (15 mg/kg/d) + Encorafenib (75 mg/kg/d) (Bini + Enco), TNO155 (10 mg/kg/d) (TNO), or Binimetinib (15 mg/kg/d) + Encorafenib (75 mg/kg/d) + TNO155 (10 mg/kg/day) (B + E + TNO). F BVR-O-15 PDX treated with Vehicle, Binimetinib (15 mg/kg/d) + Encorafenib (50 mg/kg/d) (Bini+Enco), TNO155 (10 mg/kg/d) (TNO), or Binimetinib (15 mg/kg/d) + Encorafenib (50 mg/kg/d) + TNO155 (10 mg/kg/day) (B + E + TNO). One B + E + T-treated mouse (BVR-O-15) reached a humane end-point on Day 20. Data are presented as mean values +/− SEM. One-way ANOVA on AUC with Tukey correction for multiple comparisons. Corresponding immunoblots of tumors taken at experimental endpoint from mice bearing PDXs G BVR-O-12, H BVR-O-13, and I BVR-O-15 that were treated with Vehicle, TNO155, Bini + Enco, or Bini + Enco + TNO. Each lane represents protein lysate from a different biological replicate (tumor). J MPAS calculated from the RNA expression of 10 genes comprising the MPAS signature from BVR-O-12 (triangle), BVR-O-13 (square), and BVR-O-15 (circle) treated with Vehicle, TNO155, Bini + Enco, or Bini+Enco+TNO. N = 2 biological replicates per treatment group for each PDX model. The box plot indicates the 25–75th percentiles (box boundary), median values (line in box), and min/max values (whiskers). One-way ANOVA with Tukey correction for multiple comparisons. Source data is located in the Source Data file.

Discussion

Here, we describe the results of the investigator-initiated, Phase II BEAVER trial that evaluated the efficacy of BRAF/MEK inhibition in patients with advanced cancer with non-V600E BRAF mutations. We observed minimal evidence of clinical efficacy of this regimen in a mixed patient population with a wide array of metastatic tumor types. Due to challenges with accrual during the pandemic, this trial did not meet the planned enrollment; therefore, we cannot make any definitive statement regarding the efficacy of this regimen in this patient population. We did, however, confirm the established safety profile of this therapeutic regimen, and we identified clinical, genomic, and transcriptomic characteristics that are associated with response and resistance to B + E. These data could be useful in identifying specific sub-populations wherein this regimen may be more effective.

For example, we found that 3/5 patients with Class 2 and 3 BRAF mutant melanoma experienced tumor regression with B + E. While our study enrolled a small cohort of melanoma patients, this observation is supported by a separate clinical trial wherein 75% (3 out of 4) patients with Class 2 melanoma experienced a PR with low dose dabrafenib + trametinib³³. However, another recent clinical trial enrolled patients with activating Class 2 BRAF mutations and fusions to receive B + E. This study included 3 melanoma patients, none of whom experienced a response³⁴. These clinical observations were reinforced by our in vitro experiments demonstrating that Class 2 BRAF mutant melanoma are more MAPK-dependent than non-melanoma tumor types. B + E induced more apoptosis in melanoma vs. non-melanoma cells and more potently repressed E2F and G2M gene sets in melanoma vs. non-melanoma cells. Enhanced MAPK pathway activity in the presence of B + E was a common feature of B + E-resistant melanoma but not of non-melanoma cells. Amongst melanoma patients who initially experienced tumor regression with B + E (BVR-M-05; BVR-M-08), newly acquired MAPK activating mutations in NRAS were observed at the time of disease progression, highlighting the MAPK-dependence of these tumors. Together, these data suggest that melanomas with Class 2 or 3 BRAF mutations may be more MAPK-dependent than other tumor types, and that this MAPK pathway-dependence may confer increased sensitivity to B + E. However, even amongst responders with melanoma, the duration of response or disease control with B + E was short and newly acquired resistance mutations developed quickly, highlighting the need for alternative therapeutic strategies to be developed.

The majority (86%) of patients did not respond to B + E treatment and we did not observe any newly acquired MAPK mutations at the time of progression in non-responders. These findings highlight that most tumors with Class 2 and 3 BRAF mutations may not be exquisitely dependent on MAPK pathway activity for tumor growth. Indeed, alterations in genes that regulate cell cycle progression were associated with intrinsic resistance to B + E and one patient, BVR-O-13, who experienced PD as best response, also developed a new loss-of-function mutation in the tumor suppressor RB1 at the time of progression. Moreover, p53 is a critical regulator of cell-cycle arrest in the context of cellular stress and DNA damage³⁵ and TP53 mutations have been implicated in mediating resistance to targeted therapy³⁶. TP53 was mutated in nearly 40% of tumors from patients on the BEAVER trial and all patients with TP53 mutations experienced PD as best response, whereas none of the patients who experienced PR had a TP53 mutation. Together, these data suggest that co-occurring alterations in cell cycle genes may uncouple the MAPK pathway from cell cycle regulation and nominate cell cycle mediators as potential therapeutic targets in these tumors.

We showed that CDK4/6 inhibitors could be combined with BRAF/MEK inhibitors, and this led to more cell growth inhibition in vitro and tumor growth inhibition in vivo in Class 2 and 3 BRAF mutant tumors. This therapeutic strategy may be particularly relevant in tumors where cell cycle progression is not uniquely regulated by the MAPK pathway. For example, in tumors where alterations in cell cycle regulators are present or in tumors with co-occurring MAPK-independent oncogenic alterations. These include RAS-PI3K-AKT pathway alterations, such as those present in the BVR-O-04, BVR-O-13, and BVR-O-17 PDXs, that were more responsive to B + E + P triple therapy vs. B + E alone. The combination of BRAF/MEK/CDK4/6 inhibition has previously been reported to be more effective than BRAF/MEK inhibition alone in preclinical models of Class 1 BRAF mutant melanoma, in part by modulating the tumor immune microenvironment³⁷. Currently, there is one on-going clinical trial evaluating the combination of binimetinib, encorafenib, and palbociclib in patients with BRAF V600 mutant metastatic melanoma (NCT04720768). Our preclinical data support the clinical investigation of this regimen for Class 2 & 3 BRAF mutant cancers.

Several SHP2 inhibitors, including TNO155 are actively being investigated in clinical trials (NCT03114319, NCT03634982) for various types of cancers. We found that SHP2 inhibitors - when combined with BRAF/MEK inhibitors—could potentiate tumor regression and delay resistance, even in Class 3 BRAF mutant PDX models that were intrinsically resistant to B + E. Indeed, B + E + T led to a more profound inhibition of MAPK activity in Class 3 tumors compared to B + E treatment alone. It has been reported that some RAS mutations render tumors resistant to SHP2 inhibitors; however, other KRAS mutant tumors are sensitive to Shp2 inhibitors^7,38–40. Indeed, we found that multiple RAS co-mutated Class 3 BRAF mutant cancer cells were non-responsive to the SHP2 inhibitor monotherapy. Interestingly, however, the addition of MAPK inhibitors potentiated responsiveness to TNO155 in NRAS G12D mutant WM3670 melanoma cells and in NRAS G12V or KRAS A59T co-mutated colorectal cancer PDXs. These findings suggest that SHP2i/MAPKi combinations may be effective even in RAS co-mutated Class 3 BRAF mutant tumors. Two patients with Class 3 BRAF mutations initially experienced tumor regression accompanied by ctDNA clearance, but developed new MAPK mutations at the time of disease progression. BVR-O-07 developed a new RAF1 mutation and two new MAP2K1 mutations that all require RAF-dimerization, which is a SHP2/RAS-dependent process. A second patient with a Class 3 BRAF mutation developed 5 new NRAS mutations at the time of progression. We have shown that SHP2i/MAPKi combinations remain effective even in NRAS co-mutated models of Class 3 BRAF mutant cancer. Thus, theoretically, all these newly acquired MAPK-activating mutations would remain sensitive to SHP2i/MAPKi combinations, strengthening the rationale for pursuing this therapeutic strategy in patients with Class 3 BRAF mutant cancer. One note of caution, however, is that this triple therapy was associated with more weight loss in mice compared to B + E alone, and thus alternative dosing schedules or alternative SHP2 inhibitors may be required to mitigate enhanced toxicity. Additionally, preclinical studies are advised to define the therapeutic index of this combination therapy, providing guidance for establishing initial starting doses in clinical trials that are tolerable.

Limitations of this study include the relatively small sample size and the fact that we did not complete enrollment of the BEAVER trial due to poor accrual.

The genomic complexity of Class 2 and 3 BRAF mutant tumors, relative to Class 1 BRAF mutant tumors, remains an important therapeutic challenge. Our findings suggest that many Class 2 and 3 BRAF mutant cancers can readily develop MAPK-dependent and MAPK-independent mechanisms of therapeutic resistance. Together, these data demonstrate that MAPK inhibition alone—even with novel and emerging next-generation MAPK inhibitors—may not yield deep and sustained therapeutic responses in these tumors. Future clinical trials aimed at developing precision therapies for Class 2 and 3 non-V600 BRAF mutations should incorporate inhibitors of proteins that regulate additional pathways beyond the MAPK pathway. Our data highlight CDK4/6 and SHP2 as viable therapeutic targets for future drug development strategies for these tumors.

Methods

BEAVER trial study design

The BEAVER trial (NCT03839342) was approved by the University Health Network Research Ethics Board (18-6324.6). The study design and conduct complied with all relevant regulations regarding the use of human study participants and was conducted in accordance with the criteria set by the Declaration of Helsinki. The BEAVER trial was designed to test the safety and efficacy of binimetinib and encorafenib in patients with non-V600E BRAF mutations. Key eligibility criteria were: patients with advanced solid tumors with non-V600E activating (Class 1 and 2) or inhibitory (Class 3) BRAF mutations, and no prior BRAF/MEK inhibitors. This was a single-arm, open-label study. Patients were recruited from medical oncology clinics at the Princess Margaret Cancer Center, and informed consent was obtained prior to screening and enrollment. All patients received binimetinib (45 mg PO BID) and encorafenib (450 mg PO daily) on a 28-day cycle until intolerable toxicity or progression.

The primary objective was to evaluate the objective response rate (ORR) as per RECIST 1.1 criteria⁴¹. Secondary objectives were to evaluate: progression free survival (PFS), overall survival (OS), and disease control rate (DCR). Exploratory objectives were to: (1) evaluate the dynamic changes and molecular evolution of circulating tumor DNA (ctDNA) profiles, before during and after treatment with binimetinib and encorafenib, (2) establish patient-derived xenograft (PDX) models of advanced solid tumors with non-V600E BRAF mutations, (3) evaluate biomarkers of response, and to identify molecular mechanisms of resistance to binimetinib and encorafenib in tumors with non-V600E BRAF mutations.

The BEAVER trial was a Simon 2-stage trial with the following statistical parameters: P0 = 0.05, P1 = 0.25, Alpha = 0.05, Power = 0.80 (Min-Max n = 26–33). Seven patients were planned to be enrolled in the first stage. If 1 of 7 patients enrolled in the first stage achieved an objective response, the trial would advance to the second stage. In the second stage, up to 19 patients will be enrolled. If 4/26 patients enrolled in the entire study population achieved an objective response, the study drugs would be considered worthy of further evaluation. Additional details are provided in the Clinical Trial Protocol found in the Supplementary Information (Supplementary Note).

Statistical analyses of clinical data

Differences in objective responses according to clinical and genomic variables were assessed using Fisher’s exact test. Differences in tumor measurements according to genomic variables were assessed using an unpaired T-test. PFS and OS were visualized with a Kaplan–Meier curve and differences were assessed with a log-rank test. Statistical analyses were performed using Stata/MP v17.0.

Sequencing of patients’ tumors and ctDNA

All patients enrolled on the BEAVER trial provided tumor tissue from archival specimens or from biopsies of metastatic tumors obtained prior to treatment initiation. Fresh tumor biopsies samples were collected with an 18-gauge core needle using standard surgical techniques. FFPE tissue. Genomic DNA and RNA were co-isolated from FFPE using standardized procedures with the Maxwell RSC RNA FFPE kit (Promega) in the Advanced Molecular Diagnostics Laboratory (AMDL) at the University Health Network in Toronto, ON. Sequencing of tumor tissue was performed using either the Oncomine Comprehensive Assay v3 (OCAv3) or the Illumina TruSight Oncology 500 (TSO500) assay. The OCAv3 and TSO500 assays evaluate 161 and 532 relevant cancer driver genes, respectively. For samples analyzed with the OCAv3 assay, sequencing was performed on the Ion S5 XL System and data analysis was performed using the Ion Reporter (ThermoFisher). Variant annotations were obtained from OncoKB. For samples analyzed with the TSO500 assay, Sequencing was performed using the Illumina sequencing platform at the AMDL. Variant calls were generated using a custom bioinformatics pipeline with alignment to genome build GRCh37/hg19. Variant interpretation is based on results returned by the Qiagen QCI platform (v 7.1.20210428) along with searches of cancer variant databases and biomedical literature.

Cell-free (cfDNA) was extracted from blood plasma using MagMAX Cell-Free DNA Isolation kit and analyzed using the Illumina TSO500 ctDNA targeted hybrid capture based next generation sequencing assay. Sequencing was performed using the Illumina sequencing platform at AMDL. The coding regions and 5 bp of intronic regions were analyzed for variants. Variant calls were generated using the Illumina DRAGEN pipeline with alignment to genome build GRCh37/hg19. Variant interpretation is based on results returned by the Qiagen QCI platform (v 9.2.0.20230922) along with searches of cancer variant databases and biomedical literature. Minimal acceptable coverage for all reported variants was >800×. Variants of established, potential, or uncertain clinical significance - considered Tier I, Tier II, and Tier III variants, respectively—were reported.

Generation of PDX models

BVR PDX models were generated using fresh tissue from needle biopsies of metastatic tumors from patients enrolled on the BEAVER trial. Tissue fragments were implanted into the flanks of 3 female NSG mice. Mice were monitored for tumor development by caliper measurement. P0 tumors that grew were harvested and viable fragments were frozen or implanted as a P1 passage into female SCID mice. GCRC PDXs were developed using fresh tissue of melanoma metastases¹⁵. Fragments were implanted subcutaneously in male NSG mice. All xenograft studies were designed and conducted following the institutional animal care guidelines, according to a protocol approved by the UHN Animal Care Committee or the McGill Comparative Medicine and Animal Resources Centre. The maximal allowed tumor size was 2000 mm³ and mice were euthanized if tumor volumes exceeded this size.

In vivo drug treatment experiments

For drug-treatment experiments with BVR PDXs, SCID mice were implanted with early passage (P1–P3) PDXs and monitored until the tumor volume reached ~100 mm³. Mice were then randomized to an indicated drug treatment regimen. All drug treatments were given by once or twice daily oral gavage. Drug treatment was held if BVR PDX-bearing mice lost >20% weight until they regained it. Tumor volume (V) was calculated as V = (length × width²)/2. The body weight of each mouse was recorded every 2 to 3 days. Mice were sacrificed at the days indicated or earlier if they reached a humane end-point. For each PDX model, Vehicle and Bini + Enco mice were reused in different tumor growth curves within the manuscript. Xenograft studies were designed and conducted following the institutional animal care guidelines, according to a protocol approved by the UHN Animal Care Committee. For GCRC PDXs, tumor fragments were explanted into cell culture and cells were counted. 1 million cells were implanted subcutaneously bilaterally (GCRC-Mel1) or unilaterally (GCRC-2015) into the flanks of female NSG mice (NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ), Strain No. 005557 procured from the Jackson Laboratory at 6 weeks of age. The animal housing has a 12 h light/12 h dark cycle with a room temperature between 18 and 24 °C and relative humidity between 30 and 70%. This study followed the institutional animal care guidelines, according to a protocol approved by the McGill Comparative Medicine and Animal Resources Centre. Treatment and tumor growth assessment was performed as described above for the BVR PDXs.

Cell culture and generation of resistant cell lines

A375 (CRL-1619), SkMel28 (HTB-72), RKO (CRL-2577), HT29 (HTB-38), H2087 (CRL-5922), H1666 (CRL-5885), and H508 (CCL-253) were purchased from ATCC. HT55 (C919Q16) were purchased from Sigma. WM3629 (WM3629-01-0001) and WM3670 (WM3670-01-0001) were purchased from Rockland. MDA-MB-231, 22RV1, HMV-II, FM95 cells were from Dr. Peter Siegel. All cell lines were authenticated by whole-exome sequencing (Novogene). A375, H1666, H508, WM3629, FM95, HMV-II, H2087, and WM3670 cells were cultured in RPMI (Wisent, cat. no. 350-000CL) containing 10% FBS (5% FBS was used for H2087 cells) (Wisent, cat. no. 080-450) and 1% PS (Wisent, cat. no. 450201EL). SkMel28 and MDA-MB-231 cells were cultured in DMEM (Wisent, cat. no. 319-005-CL) containing 10% FBS (5% FBS was used for MDA-MB-231 cells) and 1% PS. RKO and HT55 cells were cultured in EMEM (Wisent, cat. no. 320-005-CL) containing 10% FBS and 1% PS. HT29 cells were cultured in McCoy’s 5 A (Wisent, cat. no. 317-010-CL) containing 10% FBS and 1% PS. Resistant FM95 and HMV-II cells were cultured in media supplemented with 1% GlutaMAX (ThermoFisher, cat. no. 35050061). 22RV1, MDA-MB-231, and HMV-II binimetinib and encorafenib-resistant lines were generated by treating parental cells with increasing concentrations of binimetinib and encorafenib over a period of 8–12 months. The FM95 binimetinib and encorafenib resistant line was generated by seeding cells at low density and treating them with a high dose for approximately 2 months. Following colony formation, single cell clones were further seeded in a 96-well plate to be expanded. 22RV1 and FM95 resistant lines were maintained in 500 nM encorafenib and 100 nM binimetinib. MDA-MB-231 and HMV-II resistant cells were maintained in 1000 nM encorafenib and 200 nM binimetinib. Parental cells were also kept in culture during this period and treated with DMSO as control. Resistance was confirmed with binimetinib and encorafenib IC50 values at least 3 times greater than those of the corresponding parental cells. All cell lines used in this manuscript were routinely tested for mycoplasma by PCR (abm, cat. no. G238).

Immunoblotting

Cells were lysed with 30–100 μL of TNE lysis buffer [50 mM tris-HCl (pH 8.0), 150 mM NaCl, 1% NP-40, 2 mM EDTA, 250 mM sodium pyrophosphate dibasic, 100 mM β-Glycerophosphate disodium salt hydrate, cOmplete Mini Protease Inhibitor tablet (Sigma, cat. no. 11836153001)] was added to cells on ice. Flash-frozen tumor tissue samples (from experimental endpoint) were pulverized in liquid nitrogen using a mortar and pestle and were lysed on ice with 100–300 μL of TNE lysis buffer. Following centrifugation, protein lysates were quantified using Bradford protein reagent (Bio-Rad, cat. no. 5000006). Equal amounts of protein were loaded on 4–12% SDS-PAGE gels and were transferred onto PVDF membranes (Bio-Rad, cat. no. 1620264) by semidry transfer (Bio-Rad Trans-Blot Turbo Transfer System). Membranes were blocked (1% BSA) and incubated with primary antibodies overnight at 4 °C (see Supplementary Table 4 for antibodies and dilutions). Membranes were washed, incubated in secondary antibodies, and antibody detection was performed using Immobilon Forte Western HRP Substrate (cat. no. WBLUF0100). Bands were visualized using the Bio-Rad Chemi-Doc Imaging System or by X-ray films. For functional genomics experiments, MDA-MB-231 (transfected with non-targeting, or CDK4-targeting, or CDK6-targeting siRNA) cell pellets were harvested, washed in PBS, and stored in −80 °C until processed for protein extraction. Cells were lysed on ice for 15 min using ice-cold radioimmunoprecipitation assay buffer (1% NP-40, 150 mM NaCl, 5 mM EDTA, 50 mM Tris (pH 7.5), 0.5% deoxycholic acid, 0.1% SDS) supplied with 1× protease inhibitor cocktail (Roche, cat. no. 11836153001), NaF (5 mM), and Na3VO4 (1 mM). Standard SDS-PAGE protein separation protocol was performed, and proteins were transferred to methanol-activated Polyvinylidene fluoride (PVDF) membranes using the TransBlot Turbo transfer system, as per the manufacturer’s protocol (BioRad). Membranes were blocked for one hour in Tris-Buffered Saline (TBS) solution containing 0.1% Tween-20 (TBS-T) and 1% bovine serum albumin (Bio Basic). Membranes were then washed with TBS-T three times, 5 min each, before incubation with enhanced chemiluminescence reagent (ZmTech Scientifique) for 1 min.

DNA and RNA extraction

Parental and resistant cells were plated and the following day, treated for 24 h with DMSO or the corresponding dose of Bini + Enco that the resistant cells are grown in. Flash-frozen tumor tissue samples were pulverized in liquid nitrogen using a mortar and pestle. Tumor tissue DNA and RNA was extracted with the Zymo Research Quick-DNA Microprep Kit (cat. no. D3020) and the Qiagen RNeasy Kit (cat. no. 74134), respectively, according to the manufacturer’s instructions. RNA quantification and quality assessment was performed using the NanoDrop Spectrophotometer ND-1000 (software version 3.8.1).

WES and RNA-sequencing analysis

WES was performed on genomic DNA from all patient-derived xenografts and cell lines used through Novogene’s (Sacramento, CA, USA) WES pipeline as follows. Genomic libraries were prepared using 400 ng of genomic DNA with a SureSelect Human All Exon V6 capture kit (Agilent, Santa Clara, CA, USA) and sequenced with the NovaSeq X Plus or NovaSeq 6000 platform (Illumina, San Diego, CA, USA). The genome dataset was aligned with the human genome GCRh38 and annotated with ANNOVAR. RNA counts were obtained through Novogene’s RNAseq pipeline as follows. Messenger RNA was purified from total RNA using poly-T oligo-attached magnetic beads. cDNA was synthesized using random hexamer primers and either dUTP or dTTP based on the library. Quantified libraries were pooled and sequenced on Illumina platforms and paired-end reads were generated. Hisat2 v2.0.5 were used to build the reference genome and align the paired-end reads. Gene expression was obtained with featureCounts v1.5.0-p3. RNA counts were normalized using the DESeq2 (version 1.42.0) algorithm in R (version 4.3.2). The Wald test was used for p-value calculation and Benjamini–Hochberg false discovery rate (FDR) for the padj values. A baseMean cutoff of 50 was used for all heatmaps to filter out low counts. Gene Set Enrichment Analysis (GSEA) was performed on DESeq normalized counts with 1000 gene set permutations and comparing groups by Ratio of Classes metric. The MAPK Pathway Activation Score (MPAS) score was calculated as defined by Wagle and colleagues (2018): by adding the Z-scores for each of the 10 genes of the MPAS gene signature and dividing the total by 10, the number of genes included in the gene signature²³. The same methodology was applied to create the E2F Targets and G2M Checkpoint scores, each derived from the corresponding Hallmark MSigDB gene sets and by dividing the number of genes in the respective gene sets⁴². Statistical analysis was performed with One-way ANOVA and Tukey’s multiple comparisons test.

Clonogenic assays

Crystal violet assays were performed by seeding cells into 12- or 24-well plates and treating with indicated concentrations of drug(s) for 7–10 days¹⁵. Media with drug was replaced every 3–4 days. At experimental endpoint, cells were fixed with 10% formalin, incubated in crystal violet, and washed in water. Crystal violet stains were re-solubilized by adding 1 mL of methanol to each well and incubating at room temperature for 1 h on a rocking shaker. 100 µL of the resolubilized crystal violet solution was transferred into a 96-well plate and the relative absorbance was measured at 570 nm using a plate reader (Perkin Elmer Enspire 2300). Assays were performed at least in triplicates.

Functional genomics

siRNA transfections

2.5 × 105 MDA-MB-231 cells were seeded in 6 well-plates and transfected, the following day, with the indicated concentrations of non-targeting, or CDK4-targeting, or CDK6-targeting siRNA (siGENOME SMARTPool; Dharmacon) using Lipofectamine 2000 (Thermo Fisher Scientific), as per the manufacturer’s protocol. The following day, cells were trypsinized and 5 × 103 cells from each condition were seeded in 48-well plates and incubated at least for 6 h before adding DMSO or Binimetinib + Encorafenib at the indicated concentrations. After adding the drug treatments, plates were immediately placed in the IncuCyte, where the confluence of cells in each condition was monitored over 72 h. In parallel to seeding cells for the proliferation assays, the remaining of siRNA-transfected cells were re-seeded and maintained in 6 well-plates for the same period (72 h) to validate the siRNA efficiency at the experimental endpoint by immunoblotting.

Two-color CRISPR competition assays

This assay was performed as detailed previously⁴³ with some modifications. HEK293 FT cells were transfected with the LentiCas9-Blast vector (Addgene #52962) for lentiviral production. 22RV1, HMV-II, MDA-MB-231, and NCI-H1666 cells were transduced with the produced viruses and selected with blasticidin (2–8 µg/ml) for 5 days to generate a stable Cas9-expressing cell line.

A non-targeting (NT) gRNA in addition to two gRNAs targeting the gene of interest (AR, NRAS, KRAS, PTPN11, Supplementary Table 5) were cloned individually in Lentiguide-gRNA-NLS-GFP-2A-PURO plasmid (Addgene #185473; provided by Dr. Stephane Angers). The LentiGuide-puro-NLS-mCherry plasmid (Addgene #185474) expressing a gRNA targeting the safe harbor AAVS1 locus was provided by Dr. Stephane Angers⁴⁴. HEK293-FT cells were transfected with these vectors independently to produce lentiviruses. Cas9-expressing cells were then transduced with the produced viruses for 24 h followed by a media change, and were left to recover for two additional days prior to puromycin selection (1.5–3 µg/ml) for 4 days. After selection, cells were seeded at cell line-specific densities in 12-well plates and incubated overnight: 50 × 103 AAVS1 gRNA cells were seeded with 50 × 103 NT gRNA or target gene gRNA(s) (AR, NRAS, KRAS) in 22RV1, HMV-II, and MDA-MB-231 cells, respectively. 75 × 103 H1666-NT gRNA or H1666-PTPN11 gRNA(s) were seeded with 75 × 103 H1666-AAVS1 gRNA. The following day, plates were imaged by the G/O/NIR optical module of IncuCyte SX5 (Sartorius) to define the GFP-positive and mCherry-positive cell count/representation as a reference timepoint (passage 0; P0). For the 22RV1 CRISPR competition assay, cells were maintained in DMSO or Bini+Enco treatment (Bini 20 nM + Enco 100 nM), split 1:5 every 4–6 days and propagated in a new 12 well-plate. For the HMV-II CRISPR competition assay, cells were treated with DMSO or Bini 5 nM + Enco 25 nM and were passaged 1:5 every 4–-6 days. MDA-MB-231 cells were treated with DMSO or Bini 25 nM + Enco 125 nM and split 1:5 every 6–7 days. For the H1666 CRISPR competition assay, cells were split 1:10 every two days and propagated in a new 12 well-plate. Cells were split again in the following days if they passed 50% confluence until they were imaged at Passage 2 (22RV1, MDA-MB-231, H1666) or Passage 3 (HMV-II) as the endpoint.

Proliferation assays

siRNA transfection experiment

The IncuCyte software was used to define the percentage of cell confluence in each condition and the relative change in confluence at 72 h (endpoint) in reference to the earliest time point was calculated. The resulting values for all the experimental conditions were normalized to the cells transfected with non-targeting siRNA and treated with DMSO (the reference condition). Values from 3 independent biological replicates were combined and plotted in GraphPad Prism. Two-way ANOVA test (multiple comparisons; Tukey correction) was used to compare different conditions and the resulting p-values are stated on the graph.

Two-color competition assay

The IncuCyte Software was used to quantify the green (GFP-positive) and orange (mCherry-positive) cells in every experimental condition. A stringent analysis threshold was used to eliminate objects that are positive for the two colors, hence ensuring accurate quantifications. The percentage of GFP-positive cells from the total population was then calculated for the P0 and P2/P3 endpoint samples. The relative change in this percentage was calculated for different experimental conditions (e.g., NT gRNA, PTPN11 gRNA1, PTPN11 gRNA2), and plotted using GraphPad Prism (GraphPad Software, San Diego, CA). Two-way ANOVA test (multiple comparisons; BYK correction) was used to compare different conditions with or without Enco + Bini treatment after combining the results of 3 biological replicates (from three independent gRNA transductions) and the resulting p-values are stated in the figure legend. For the two-color competition assay targeting PTPN11, different conditions were compared using a two-tailed t-test after combining the results of 3 biological replicates (from three independent gRNA transductions).

Cell-cycle analysis

Cell cycle analysis was performed by plating 350,000–500,000 cells per well of a 6-well plate for a total of ~1,000,000 cells per condition. 4–6 h later, cells were synchronized by aspirating media, washing 2× with PBS and incubated in low serum conditions (0.5% FBS) overnight. The following day, cells were treated with inhibitors for 24 h and then washed twice with ice-cold PBS containing 1% FBS. Cells were stained with 50 mg/mL propidium iodide solution in hypotonic buffer (0.1% Triton ×-100 and 0.1% sodium citrate) for at least 20 min in the dark. A minimum of 20,000 cycling cells were analyzed using BD FACSCanto II flow cytometer (REF 338960). Data was analyzed using ModFit LT for Windows software (v 4.1.7). Diploid cell cycle phases were used for analyses.

Apoptosis assays

Drug-induced apoptosis was assessed by annexin V combined with propidium iodide (PI) staining. Cells and supernatants were collected following 72 h treatment and stained with Annexin-V-FITC Detection kit (BD Pharmingen, cat. no. 560931) and measured on a BD FACSCanto II flow cytometer.

RAS activity assay

The levels of activated RAS-GTPase were determined using the Active Ras Detection Kit (Cell Signaling, cat. no. 8821), as per the protocol. Briefly, cells (HMV-II 1,200,000 cells/dish, 22RV1 2,000,000 cells/dish, FM95 1,200,000 cells/dish, MDA-MB-231 1,200,000 cells/dish) were seeded in a 10 cm dish (Falcon 100 × 20 mm, cat. no. 353003) and left overnight to attach. Two dishes per cell line were treated with DMSO or Bini (100 nM) + Enco (500 nM) for 24 h. Dishes were put on ice and cells were lysed with 1× lysis buffer (Cell Signaling, cat. no. 11524). GTP-bound RAS was quantified using RAF1 Ras-binding domain (RBD) pulldown. GTP-bound RAS and input samples were then used for immunoblotting for RAS. Western blot was quantified using Fiji and GTP-bound RAS was normalized over total RAS normalized to loading.

Co-Immunoprecipitation assay

Cells were seeded and treated for 24 h following the same protocol as the RAS activity assay. Dishes were placed on ice, washed in PBS and 100 μL of IP lysis buffer [25 mM Hepes-NaOH (pH 7.5), 115 mM Potassium Acetate, 1 mM EDTA (pH 8), 1% NP-40, 250 mM sodium pyrophosphate dibasic, 100 mM β-Glycerophosphate disodium salt hydrate, cOmplete Mini Protease Inhibitor tablet (Sigma, cat. no. 11836153001)] was added to each dish. Dishes were placed on the rocker at 4 °C for 15 min, then cells were scrapped and centrifuged at 12000 rpm for 15 min. Protein was quantified as described previously and 500 μg of each sample was incubated with either BRAF or CRAF antibody for 1.5 h on a rotator at 4 °C. Samples were centrifuged, 100 μL of Pierce™ Protein A/G Agarose were added and samples were left on a rotator at 4 °C overnight. Samples were washed 3× with IP lysis buffer and 25 μL of 4× loading dye was added to the bound fraction. Samples were boiled at 100 °C for 10 min and centrifuged for 10 min at 12000 rpm. 10 μL of the bound fraction was used for immunoblotting. Western blot was quantified using Fiji and normalizing the amount of CRAF pulled down in each BRAF IP.

3D spheroid formation and proliferation

Cells (WM3670: 2500 cells/well; WM3639: 10,000 cells/well) were seeded in round bottom ultra-low attachment 96-well plates (Corning, cat. no. 7007) and incubated for 72 h at 37 °C in 5% CO₂. Spheroids were treated in duplicate with the indicated concentrations of drug(s) for 5 days. Images were acquired using the EVOS M5000 Imaging System (Thermo Fisher) at 4× magnification; scale bars represent 300 μm. Cell viability was determined using the XTT assay kit (Cell Signaling, cat. no. 9095), following the manufacturer’s instructions. Absorbance was read using a plate reader (Perkin Elmer Enspire 2300). Assay data was normalized to DMSO values and were performed at least in triplicates.

Immunohistochemistry and image analysis

Hematoxylin and eosin (H&E) staining was performed on paraffin-embedded (FFPE) sections using the Leica XL Autostainer (Leica). Slides were deparaffinized in two changes of xylene (4 min each), rehydrated through graded ethanol (100%, 95%, 70%, 50%, 30%) to water, and stained with Harris hematoxylin (Sigma Aldrich, #HHS128) for 5 min. Differentiation was carried out with 1% acid alcohol (15 s), followed by bluing in 0.1% ammonia water for 45 s. Slides were then counterstained with 0.1% Eosin Y (Sigma Aldrich, #HT1103128) for 2 min, dehydrated through graded ethanol, cleared in xylene, and mounted with permanent mounting medium. Digital scanned images of the slides were captured using the Hamamatsu Nanozoomer 2.0 HT scanner. Paraffin-embedded sections were deparaffinized and rehydrated via sequential immersion in xylene and graded alcohol solutions, followed by incubation in 3% hydrogen peroxide for 15 min to inhibit endogenous peroxidase activity. Following a 10-min room temperature block with animal-free blocker (Vector Laboratories, #SP-5030) diluted in Tris-buffered saline with Triton ×-100, the sections were incubated with primary antibodies for 1.5 h at room temperature, including phospho-p44/42 MAPK (Erk1/2) (Thr202/Tyr204) (D13.14.4E) rabbit monoclonal antibody (Cell Signaling Technology #4370) at 1/40 dilution. After washing, the sections were exposed to the secondary antibody, horse anti-rabbit IgG-HRP reagent (Vector Laboratories, #MP-7401) at room temperature for 35 min. Visualization of target proteins was facilitated by incubation with 3,3′-diaminobenzidine (DAB) substrate, followed by counterstaining with CAT hematoxylin (Biocare Medical, #CATHE) before drying and mounting with EcoMount (BioCare Medical, #EM897L). Digital scanned images of the slides were captured using the Hamamatsu Nanozoomer 2.0 HT scanner. Comprehensive image analysis was performed using HALO software v.3.4 (Indica Labs). Tumor areas annotated by a pathologist (Z. S. K.) were imported. pERK and hematoxylin intensities were detected using Multiplex IHC v.3.1.4 algorithms to identify positive cells according to DAB staining status. The percentage of pERK positive cells in annotated tumor areas was calculated.

Statistics and reproducibility

No statistical method was used to predetermine sample size for preclinical experiments. In vitro experiments were performed a minimum of N ≥ 3 times with similar results. Data were analyzed using the appropriate statistical tests for each dataset, with the specific test used indicated in each figure legend. Statistical analyses of clinical data were performed using Stata/MP v17.0 (College Station, TX), while statistical analyses of preclinical data were performed using GraphPad Prism 10 (GraphPad Software, Boston, MA).

Reporting summary

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

Supplementary information

Supplementary Information^{(12.5MB, pdf)}

Reporting Summary^{(105.5KB, pdf)}

Transparent Peer Review file^{(793.9KB, pdf)}

Source data

Source Data^{(84.9MB, zip)}

Acknowledgements

The BEAVER clinical trial was sponsored by the Cancer Genomics Program of the Princess Margaret Cancer Centre. This investigator-initiated trial was supported by Pfizer, which supplied the study drug and funding for clinical operations. Pfizer reviewed and approved the study protocol and manuscript but had no involvement in data collection or analysis. Exploratory objectives and preclinical experiments were funded by a Conquer Cancer Foundation Young Investigator Award and a Career Development Award to AANR, a Canadian Cancer Society Challenge Grant (#707457) to A.A.N.R. and A.S., Canadian Institutes of Health Research (CIHR) Project Grants (#180379, #197819) to A.A.N.R., and start-up funding from the TransMedTech Institute and Apogee Canada Research Excellence Fund and the Jewish General Hospital Foundation to A.A.N.R. Additional infrastructure support to carry out this research was provided by a John R. Evans Leaders Fund award (#42153) from the Canada Foundation for Innovation to A.A.N.R. with matched funds from the Province of Quebec. A.A.N.R. acknowledges salary support from a Fonds de Recherche du Québec—Santé (FRQS) Clinical Research Scholar Award; J.M. is the recipient of an Elizabeth Steffen Memorial Fellowship from McGill University Faculty of Medicine and Health Sciences and a FRQS doctoral award and a CIHR doctoral award, E.R. is a recipient of a Marathon of Hope Data Science Award, a FRQS doctoral award, and a CIHR doctoral award, M.R. and I.S.B. are recipients of CIHR postdoctoral fellowships; C.M. is a recipient of a CIHR Canada Graduate Scholarships-Masters (CGS-M) award and a Masters Research Training Award from the Canadian Cancer Society (CCS) and a Cancer Research Society Doctoral Research Award. IEE is a Peter Quinlan Fellow in Oncology from McGill University Faculty of Medicine and Health Sciences and a recipient of a CCS postdoctoral fellowship. We are thankful to staff members of the Cancer Genomics Program who were involved with administration and coordination of the BEAVER trial including: Celeste Yu, Elizabeth Shah, Samanta del Rossi and Sam Felicen, as well as technical support from Peter Tai, Lucy An, and Dr. Huijie Wang for pre-clinical experiments. We are especially grateful to all of the patients (and their families) who volunteered to enroll in this study.

Author contributions

Conceptualization: A.A.N.R., L.L.S., A.S. Data collection: A.A.N.R., J.M., E.R., C.L.M., I.E.K., M.R., M.B., E.C., I.S.-B., C.T., M.M., R.W.Y.L., A.J.E., B.X.W., I.K., T.Z., Z.S.K., M.B., F.A.S., N.B.L., A.A.R., A.H., S.D.S., P.L.B., P.M.S., L.L.S., D.W.C., A.S. Formal analysis: A.A.N.R., J.M., E.R., C.L.M., I.E.K., B.X.W., A.S. Funding acquisition: A.A.N.R., L.L.S., D.W.C., A.S. Methodology: A.A.N.R., J.M., E.R., C.L.M., I.E.K., M.R., I.S.-B., B.X.W., T.Z., T.J.P., L.L.S., D.W.C., A.S. Project administration: A.A.N.R., P.S., L.L.S., D.W.C., A.S. Resources: A.A.N.R., P.S., L.L.S., D.W.C., A.S. Supervision: A.A.N.R., P.S., D.W.C., A.S. Writing original draft: A.A.N.R., J.M., E.R., C.L.M., I.E.K., T.J.P., F.A.S., P.M.S., D.W.C., L.L.S., A.S. Writing revised draft: A.A.N.R., J.M., E.R., C.L.M. Review and approval of manuscript: all authors.

Peer review

Peer review information

Nature Communications thanks Meenhard Herlyn, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

Data availability

The BEAVER trial protocol is available with this submission as a supplementary note in the supplementary information file. Data supporting the findings presented in this study are available in the article, supplementary information, source data document or the supplementary source data file for the Supplementary Figures. RNA sequencing data from PDX and cell lines used in this manuscript have been uploaded to the Gene Expression Omnibus database (GSE308451). https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE308451 The whole exome sequencing performed on the PDX samples have also been made publicly available and can be found under the same BioProject (PRJNA1330616). https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1330616. Deidentified patient-level data on clinical responses are provided in the source data document. Requests to access any additional raw data should be forwarded to the corresponding authors at april.rose@mcgill.ca and/or anna.spreafico@uhn.ca. All requests for data and materials will be reviewed to verify whether the request is subject to any intellectual property or confidentiality obligations. Any data that can be shared will be released via a Material Transfer Agreement. Source data are provided with this paper.

Competing interests

Dr. Rose has provided consultation for Advanced Accelerator Applications/Novartis, EMD Serrono, Merck, and Pfizer. Dr. Rose reports research funding from AstraZeneca (Inst), Novartis (Inst), Merck (Inst), Seattle Genetics (Inst), Pfizer (Inst), and Essa Pharma (Inst). Dr. Soria Bretones is an employee of Repare Therapeutics. Dr. King reports research funding from Pfizer (Inst). Dr. Pugh has provided consultation for AstraZeneca, Chrysalis Biomedical Advisors, and Merck (compensated); and receives research support (institutional) from Roche/Genentech and AstraZeneca. Dr. Shepherd has provided consultation for AstraZeneca and Data and Safety Monitoring Board activity for Merck and Celltrion. Dr. Leighl reports Research Funding and/or study materials (Institution)from: Amgen, AstraZeneca, Boehringer Ingelheim, BMS, Eli Lilly, GlaxoSmithKline, Janssen, MSD, Novartis, Pfizer, Takeda, Guardant Health, Neogenomics; Honorarium for CME lecture (to institution) from: Amgen; funding for travel, accommodations (CME lectures) from: AstraZeneca, Dava, Guardant Health, Johnson & Johnson, MSD, Roche; and uncompensated Data Safety Monitoring Board activity for: Mirati Therapeutics, Daiichi Sankyo. Dr. Razak reported a consulting/advisory role with Adaptimmune, Bayer, GlaxoSmithKline, Medison, Inhibrx, research funding from Deciphera, Karyopharm Therapeutics, Pfizer, Roche/Genentech, Bristol Myers Squibb, MedImmune, Amgen, GlaxoSmithKline, Blueprint Medicines, Merck, AbbVie, Adaptimmune, Iterion Therapeutics, Neoleukin Therapeutics, Daiichi Sankyo, Symphogen, Rain Therapeutics, and expert testimony with Medison. Dr. Hansen reported receiving research funds (paid to institution) from: Advancell, AVEO, BMS, Janssen, Macrogenics, MSD, Seagen, Roche, and Tyra Biosciences. Consulting fees (paid personally) from: Astellas, Bayer, Eisai, MSD. Dr. Bedard reported an uncompensated consulting/advisory role with BMS, Pfizer, Seattle Genetics, Lilly, Amgen, Merck, Gilead Sciences, Repare and research funding from Bristol Myers Squibb (Inst), Sanofi (Inst), AstraZeneca (Inst), Genentech/Roche (Inst), GlaxoSmithKline (Inst), Novartis (Inst), Nektar (Inst), Merck (Inst), Seattle Genetics (Inst), Immunomedics (Inst), Lilly (Inst), Amgen (Inst), Bicara Therapeutics (Inst), Zymeworks (Inst), Bayer (Inst), Medicenna (Inst), Day One Biopharmaceuticals (Inst). Dr. Siu reported consultant/advisory roles for: Merck, Pfizer, AstraZeneca, Roche, GlaxoSmithKline, Voronoi, Arvinas, Navire, Relay, Marengo, Daiichi Sankyo, Amgen, Medicenna, LTZ Therapeutics, Tubulis, Marengo, Nerviano, Pangea, Incyte, Gilead; Institutions receives grant/research support for clinical trials from: Merck, Novartis, Bristol-Myers Squibb, Pfizer/Seattle Genetics, Boerhinger-Ingelheim, GlaxoSmithKline, Roche/Genentech, AstraZeneca/Medimmune, Bayer, Abbvie, Amgen, Symphogen, EMD Serono, 23Me, Daiichi Sankyo, Gilead, Marengo, Incyte, LegoChem, Loxo/Eli Lilly, Medicenna, Takara; Spouse has leadership position: Treadwell Therapeutics; Spouse has stock ownership: Agios. Dr. Cescon reports consultancy and advisory relationships with AstraZeneca, Daiichi Sankyo, Exact Sciences, GenomeRx, Gilead, GlaxoSmithKline, Inivata/NeoGenomics, Lilly, Merck, Novartis, Pfizer, Roche and SAGA; research funding to their institution from AstraZeneca, GenomeRx, Guardant Health, Grail, Gilead, GlaxoSmithKline, Inivata/NeoGenomics, Knight, Merck, PearBio, Pfizer, ProteinQure, RayzeBio and Roche. Dr. Spreafico reported a consulting/advisory role with Merck, Bristol-Myers Squibb, and Alents andgrant/research funding from Novartis, Bristol-Myers Squibb, Symphogen AstraZeneca/Medimmune, Merck, Bayer, Surface Oncology, Northern Biologics, Janssen Oncology/Johnson & Johnson, Roche, Regeneron, Alkermes, Array Biopharma/Pfizer, GSK, NuBiyota, Oncorus, Treadwell, Amgen, ALX Oncology, Nubiyota, Genentech, Seagen, Servier, Incyte, Alentis. All other co-authors report no conflicts of interest.

Footnotes

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: April A. N. Rose, Jennifer Maxwell, Emmanuelle Rousselle.

Contributor Information

April A. N. Rose, Email: april.rose@mcgill.ca

Anna Spreafico, Email: anna.spreafico@uhn.ca.

Supplementary information

The online version contains supplementary material available at 10.1038/s41467-025-68076-7.

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39.Chen, Y.-N. P. et al. Allosteric inhibition of SHP2 phosphatase inhibits cancers driven by receptor tyrosine kinases. Nature535, 148–152 (2016). [DOI] [PubMed] [Google Scholar]
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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Information^{(12.5MB, pdf)}

Reporting Summary^{(105.5KB, pdf)}

Transparent Peer Review file^{(793.9KB, pdf)}

Source Data^{(84.9MB, zip)}

Data Availability Statement

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[CR26] 26.Atefi, M. et al. CRAF R391W is a melanoma driver oncogene. Sci. Rep.6, 27454 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR27] 27.Riaud, M. et al. The role of CRAF in cancer progression: from molecular mechanisms to precision therapies. Nat. Rev. Cancer24, 105–122 (2024). [DOI] [PubMed] [Google Scholar]

[CR28] 28.Gao, Y. et al. Allele-specific mechanisms of activation of MEK1 mutants determine their properties. Cancer Discov.8, 648–661 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR29] 29.Sodir, N. M. et al. SHP2: a pleiotropic target at the interface of cancer and its microenvironment. Cancer Discov.13, 2339–2355 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR30] 30.Santarpia, L., Lippman, S. M. & El-Naggar, A. K. Targeting the MAPK–RAS–RAF signaling pathway in cancer therapy. Expert Opin. Ther. Targets16, 103–119 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR31] 31.Roberts, P. J. & Der, C. J. Targeting the Raf-MEK-ERK mitogen-activated protein kinase cascade for the treatment of cancer. Oncogene26, 3291–3310 (2007). [DOI] [PubMed] [Google Scholar]

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[CR37] 37.Lelliott, E. J. et al. Combined BRAF, MEK, and CDK4/6 inhibition depletes intratumoral immune-potentiating myeloid populations in melanoma. Cancer Immunol. Res.9, 136–146 (2021). [DOI] [PubMed] [Google Scholar]

[CR38] 38.Valencia-Sama, I. et al. NRAS status determines sensitivity to SHP2 inhibitor combination therapies targeting the RAS–MAPK pathway in neuroblastoma. Cancer Res.80, 3413–3423 (2020). [DOI] [PubMed] [Google Scholar]

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[CR44] 44.MacLeod, G. et al. Genome-wide CRISPR-Cas9 screens expose genetic vulnerabilities and mechanisms of temozolomide sensitivity in glioblastoma stem cells. Cell Rep.27, 971–986.e979 (2019). [DOI] [PubMed] [Google Scholar]

PERMALINK

Binimetinib and encorafenib for the treatment of advanced solid tumors with non-V600E BRAF mutations: results from the Phase II BEAVER trial

April A N Rose

Jennifer Maxwell

Emmanuelle Rousselle

Chantel L Mukonoweshuro

Islam E Elkholi

Melody Riaud

Marco Biondini

Erica Cianfarano

Isabel Soria-Bretones

Chantal Tobin

Meghan McGuire

Rhoda W Y Law

Andrew J Elia

Ben X Wang

Ian King

Tong Zhang

Trevor J Pugh

Zaid Saeed Kamil

Marcus Butler

Frances A Shepherd

Natasha B Leighl

Albiruni Abdul Razak

Aaron Hansen

Samuel D Saibil

Philippe L Bedard

Peter M Siegel

Lillian L Siu

David W Cescon

Anna Spreafico

Abstract

Introduction

Results

Clinical characteristics and outcomes of patients enrolled on the BEAVER trial

Fig. 1. Clinical efficacy of binimetinib and encorafenib in patients with non-V600E BRAF mutations.

Genomic characteristics of patients’ tumors

Development of pre-clinical models of BRAF/MEK inhibitor resistant Class 2 & 3 BRAF mutant cancers

Fig. 2. Generation and characterization of preclinical models of BRAF/MEK inhibitor resistant Class 2 and 3 BRAF mutant cancer.

Characterization of cell cycle alterations in BRAF/MEK inhibitor resistant Class 2 BRAF mutant cells

Fig. 3. Identification of altered cell cycle regulation in resistant Class 2 BRAF mutant cells.

Evaluating BRAF/MEK/CDK4/6 inhibitor combinations in Class 2 BRAF mutant cancers

Fig. 4. Evaluation of CDK4/6 + BRAF/MEK inhibition in Class 2 BRAF mutant tumors.

Identifying SHP2 as a therapeutic target in BRAF/MEK inhibitor resistant Class 3 tumors

Fig. 5. Identification of SHP2 as a therapeutic target in Class 3 BRAF mutant tumors.

Evaluating BRAF/MEK/SHP2 inhibitor combinations in Class 3 BRAF mutant cancers

Fig. 6. Evaluation of SHP2 + BRAF/MEK inhibition in Class 3 BRAF mutant tumors.

Discussion

Methods

BEAVER trial study design

Statistical analyses of clinical data

Sequencing of patients’ tumors and ctDNA

Generation of PDX models

In vivo drug treatment experiments

Cell culture and generation of resistant cell lines

Immunoblotting

DNA and RNA extraction

WES and RNA-sequencing analysis

Clonogenic assays

Functional genomics

siRNA transfections

Two-color CRISPR competition assays

Proliferation assays

siRNA transfection experiment

Two-color competition assay

Cell-cycle analysis

Apoptosis assays

RAS activity assay

Co-Immunoprecipitation assay

3D spheroid formation and proliferation

Immunohistochemistry and image analysis

Statistics and reproducibility

Reporting summary

Supplementary information

Source data

Acknowledgements

Author contributions

Peer review

Peer review information

Data availability