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

Domain: biology
Published: 2026-01-03
Editorial outcome: minor_revision

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.

Introduction
BRAF is one of the most frequently mutated actionable oncogenes in cancer1,2. 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 cancer4,6. In RAS wild-type tumors, the protein tyrosine phosphatase, SHP2, has been implicated as a key regulator of RAS activity that potentiates RAF dimerization7,8. 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 mutations15,16. 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 regimen18,19. 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 mutations18,19. 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 pre-clinical 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.

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 mutations20,21. 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 wild type 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 MAPK i 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.

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 treatments 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.

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 portal24,25 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 maybe 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 manner26,27, 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). Among 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 non-receptor 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 GTPases30,31. 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., wild type 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 previously7,32, 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.

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 (Thermo Fisher). 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 (v7.1.20210428) 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 (v9.2.0.20230922) 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 5A (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 (Thermo Fisher, 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 cultured 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 semi dry 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 Trans Blot Turbo transfer system, as per the manufacturer’s protocol (Bio Rad). 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 resolubilized 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. HEK293FT 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 time point (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 X-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 (v4.1.7). 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-GTP 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 pre-clinical 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.

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.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The BEAVER Phase II trial evaluated the efficacy of the BRAF and MEK inhibitors binimetinib and encorafenib in patients with non-V600E BRAF mutations. Only two patients with Class 2 melanoma and one with Class 3 pancreatic cancer out of 21 total cancer patients responded to the combination therapy. The authors generated PDX models and cell lines from Class 2 and 3 BRAF mutant cancers and analyzed tumor whole exome, RNA-seq and ctDNA from patients. By targeting cell cycle regulators with a combination of CDK4/6 and BRAF/MEK inhibitors, they observed improved efficacy in Class 2 BRAF mutant tumors. For Class 3 BRAF mutant cancers, SHP2 was identified as a critical therapeutic target, and combining SHP2 with BRAF/MEK inhibitors showed promising results in overcoming resistance in PDX models. These findings suggest potential combination therapies to improve outcomes for patients with non-V600E BRAF mutant cancers. Overall, the study could benefit from more granular insight into the heterogeneity of resistance mechanisms across different Class 2 and 3 BRAF mutations and cancer types, which could inform more personalized approaches. The authors may need to address further on the concerns below.\n1. Figures 2f and 3a indicate that the mechanisms of drug resistance to B+E treatments are determined by cancer type rather than by shared signaling pathways across different cancers. For instance, in Figure 2f, class 2 melanomas re-activate MAPK to acquire resistance, while breast and prostate cancer cells do not. Additionally, in the BEAVER trial, only two melanoma patients in class 2 responded to B+E treatment. Would it be more rational to focus on class 2 melanoma instead of expanding to breast, NSCLC, and colon cancers (Fig. 4a, c, d, g)?\n2. Given that the growth of parental class 2 cancer cells is significantly inhibited by B+E in vitro (Fig. 2d), the alteration of E2F targets and G2M checkpoint gene sets in resistant cell lines is not a novel mechanism. These changes are likely downstream effects rather than causative signaling mechanisms for B+E resistance.\n3. In Figures 4h-j, Palbo treatment of all three cell lines demonstrated strong activation of ERK. Could you further elucidate the possible underlying mechanism?\n4. In Figures 6a-b, the in vitro experiments are conducted using four cancer types classified as class 3. However, in Figures 6c-f, the efficacy of the combination B+E+T is validated only in PDX models of colorectal cancer. Does B+E+T also show efficacy in other cancer types?\n5. Figure and subfigure titles could be more conclusive than descriptive to enhance readability. For example, instead of a descriptive title, consider: 'Fig. 1: BEAVER trial demonstrates 14% clinical efficacy of Binimetinib and Encorafenib in patients with non-V600E BRAF mutations.'", "reviewer_2": "In the era of precision oncology, identification of novel combinatory strategies in molecularly selection population is one of the main fields of research. In this scenario, the identification of potential treatment strategies for patients with BRAF class 2 and class 3 alterations is under clinical evaluation.\nHere Rose and colleagues presented the results of the BEAVER clinical trial, exploring the combination of binimetinib and encorafneib in a cohort of patients with different tumor types harboring BRAF alterations other than class 1. While the experimental strategy has shown limited activity, the authors conducted multiple exploratory analysis to identify potential mechanisms of resistance and potential therapies.\nI would like to congratulate with the authors for their excellent manuscript!\nI have only few comments:\n- It could be of interest if the authors present data regarding the previous line of treatment since a target therapy could have an impact on the baseline molecular profile.\n- Are there any information’s regarding subsequent therapies?", "reviewer_3": "This manuscript by Rose et al is primarily focused on the study of non-V600 BRAF mutant tumors to understand molecular mechanisms of treatment response and resistance to guide the design of more effective therapies. The authors provide data from a Phase II clinical trial designed to evaluate activity of binimetinib and encorafenib targeting MEK and BRAF, respectively, to treat non-V600 BRAF mutant disease across a wide array of tumor histologies. PDX models were established from tumor biopsies to facilitate the identification of mechanisms of resistance. Two separate lines of investigation were pursued to generate preclinical data in support of combination of binimetinib and encorafenib with a third agent to improve response. First, combination of MEK/BRAF inhibitors with a CDK4/6 inhibitor to target cell cycle progression in Class 2 BRAF mutant tumors, and second, combination of these agents with a SHP2 inhibitor to address co-occurring activating mutations in RAF1 or NRAS in Class 3 tumors were explored.\nThis study addresses an area of high unmet medical need since the mean overall response irrespective of class of non-V600 BRAF mutation across the entire cohort of Phase 2 patients treated with binimetinib plus encorafenib was ~6 months. Overall, the data and studies reported here lend further support for key similarities and differences between class 2 and class 3 BRAF mutants for the design of improved precision therapies. However, it is recommended that the following deficiencies be addressed to strengthen the overall impact of this study.\n1. The Phase 1/2 study published by Yaeger and colleagues, who also evaluated clinical activity of binimetinib/encorafenib against non-V600 BRAF tumors (Rustgi et al; Oncologist 2023), should be cited. Their findings appear to corroborate the findings of the present study.\n2. Was any biopsy analysis conducted showing incomplete inhibition of ERK signaling? Since biopsies were obtained during the clinical trial, it is unclear why this was not explored, as biopsy tissue is more clinically relevant than xenografts established from a heterogeneous specimen.\n3. Figure 1b: since BRAF/MEK inhibitor combination therapy has been reported to elicit activity against BRAF V600 mutated cholangiocarcinomas, they should be distinguished from pancreatic carcinomas as opposed to all being classified as pancreaticobiliary tumors.\n4. Figure 2b: Tumor growth inhibition (TGI) needs to be defined. The text states that nine PDX models were established, but there are only eight models plotted here. The conclusion that there is an indirect correlation between clinical change in target lesion vs preclinical efficacy of the PDX model established from that lesion does not appear to be justified. Five of the eight PDX models shown in this figure originated from colorectal tumors, which were the most refractory histology evaluated (5 of 6 showing clinical progression).\n5. Figure 3g: Were PDX models not available from patients testing positive for E2F/G2M gene alteration to evaluate in vivo efficacy of binimetinib/encorafenib/palbociclib?\n6. Figure 3i: Combined knockdown of CDK4 and CDK6 does not appear to potentiate cell growth inhibition mediated by binimetinib and encorafenib at the low doses (line 360).\n7. Triple therapy combination (B+E+palbociclib) does not appear to be more effective than palbociclib alone against MB-231 cells (lines 367-369).\n8. Figure 4e-g: added benefit of combining three agents (B+E+palbociclib) does not appear to be significantly enhanced compared to palbociclib alone. Statistics should be provided comparing those two arms in each study. The absence of meaningful tumor regressions in the triple combination arms is a concern and weakens the conclusion that the preclinical data support clinical evaluation of this combination strategy for Class 2 & 3 BRAF mutant cancers (lines 538-539).\n9. The weight loss seen in the triple combination studies with B+E+SHP2 inhibitor highlights the importance of carrying out preclinical evaluation of therapeutic index of novel treatment strategies, an important concept that has not been addressed anywhere in the manuscript.", "reviewer_4": "This is a biostatistical review focused on the clinical trials component of the article.\nIn this work, the authors report the outcomes from the BEAVER clinical trial, a single-arm phase 2 trial of BRAF and MEK inhibitors in patients with non-V600E BRAF mutations. Due to challenges in accrual, the study was unfortunately not able to complete the planned accrual, nor did the investigators observe 4 responses to meet the design-specified efficacy threshold.\nI applaud the authors' careful interpretations of the primary clinical findings, and a strength of this article is the follow-up lab-based research using patient samples from the clinical trial.\nI have some revision ideas that I hope help can help a reader of this article:\n1. In the 1st Discussion paragraph summarizing the results, the authors could consider repeating that there was reduced accrual to remind the reader and contextualize the failure to meet the design-specified efficacy threshold. I liked the language in the limitations section that, because the planned enrollment was larger than the actual enrollment, it is hard to interpret the fact that only 3 responses were observed. Maybe a sentence like the following could fit into lines 487-489:\n\"We did not meet the planned enrollment total due to accrual challenges, and we also did not observe the number of responses necessary to meet the design efficacy threshold; therefore, we cannot make any definitive statement regarding the efficacy of this regimen in this patient population.\"\n(If something like this is used, please also review the limitations paragraph for any redundancy with this language.) Please also consider a similar type of statement for the abstract, where the primary efficacy conclusion (or lack thereof) did not appear to be mentioned.\n2. Related to above, the authors could consider including a reminder in the Discussion that patients on this study experienced only expected toxicities from this regimen.\nMinor comments:\n1. Could the authors clarify that all (13) patients who had pre-treatment biopsy contributed to the attempted PDX models? (If accurate?) In lines 272-273, it was unclear if there was a specific criterion for attempting the PDX or just that all available samples were used.\n2. I noticed the p-value stated on Figure S3B slightly differs from line 248."}, "author_response": "We thank the Reviewer #1 for their careful review and appraisal of our manuscript. Below we provide a point-by-point response to the issues raised by the reviewer.\nR1: 1. Figures 2f and 3a indicate that the mechanisms of drug resistance to B+E treatments are determined by cancer type rather than by shared signaling pathways across different cancers. For instance, in Figure 2f, class 2 melanomas re-activate MAPK to acquire resistance, while breast and prostate cancer cells do not. Additionally, in the BEAVER trial, only two melanoma patients in class 2 responded to B+E treatment. Would it be more rational to focus on class 2 melanoma instead of expanding to breast, NSCLC, and colon cancers (Fig. 4a, c, d, g)?\nWe agree that we did observe a trend toward increased clinical efficacy in patients with melanoma; however, the patient who derived the most benefit (longest duration of response) on the BEAVER trial was a patient with ampullary cancer, and we observed stable disease in other patients with biliary tract cancers. Furthermore, other studies have reported response to BRAF/MEK inhibitors in Class 2 or 3 BRAF mutant non-melanoma cancers (PMIDS: 35977349, 37616543). Importantly, oncogenic BRAF V600 mutations is well established as a tumor-tissue type agnostic therapeutic target for BRAF/MEK inhibitors in cancer (PMIDs: 38278874, 37159870, 36842301). Thus, based on the design of the BEAVER trial, which included patients with advanced cancer, independent of cancer type, and the fact that Oncogenic BRAF is a tumor-type agnostic tissue target, we felt it was important to evaluate mechanisms of resistance across multiple tumor types.\nIn our MDA-MB-231 breast cancer model, we did also observe sustained MAPK activity in resistant cells (Figure 2c, Figure S5e), suggesting that MAPK activity could be important for resistance in cancer types other than melanoma. Conversely, in PDX models of Class 2 BRAF mutant melanoma (GCRC2015 GCRMel1), tumors treated with B+E grew progressively (Figure 4e,f), despite sustained inhibition of the MAPK pathway at the experimental endpoint (Figure 4h,i). Indeed, we also observed a significant decrease in pERK staining in on-treatment biopsies from 6 patients (all of whom had PD or SD as best response) with matched pre/on-treatment tissue biopsies, suggesting that inhibition of MAPK activity alone may be insufficient to induce responses in many patients with Class 2/3 non-V600 BRAF mutations (new Figure S3D).\nTogether, these findings suggests that even in melanoma, some tumors may acquire resistance in a MAPK-independent fashion. In summary, we expect that some MAPK-dependent mechanisms will be shared across tumor types and that some MAPK-independent mechanisms will also be observed in multiple tumor types, and for these reasons we decided not to focus solely on melanoma.\nR1 - 2. Given that the growth of parental class 2 cancer cells is significantly inhibited by B+E in vitro (Fig. 2d), the alteration of E2F targets and G2M checkpoint gene sets in resistant cell lines is not a novel mechanism. These changes are likely downstream effects rather than causative signaling mechanisms for B+E resistance.\nWe thank the reviewer for this comment. Our data shows that multiple mechanisms of resistance to BRAF/MEK inhibitors in BRAF Class 2 cancers converge on the alteration of cell cycle regulation. We agreed with the reviewer that it would be of interest to define the upstream mediators of resistance in these models. We have performed several new experiments to address this question, and we believe this new data substantially strengthens our manuscript (updated Figure 2, updated Figure S5, new Figure S6).\nIn BRAF V600 mutant melanoma and colorectal cancer, multiple MAPK-dependent mechanisms of resistance to BRAF targeted therapy have been described (PMID: 26608120, Johnson et al., 2016; PMID: 22448344, Corcoran et al., 2012). Therefore, we first assessed whether MAPK activity was associated with resistance across the 4 resistant cell lines models. We have updated the immunoblots presented in Figure 2c to now include quantified data (new Figure S5e). With this, we find that B+E inhibits MAPK activity in HMV-II, FM95 and 22rv1 parental cells, but MAPK activity is sustained in MDA-MB-231 parental cells treated with B+E. MAPK activity (pERK) is higher in resistant FM95 and HMV-II cells vs. parental cells, but not in 22rv1 cells. Thus, acquired MAPK reactivation is observed in the 2 melanoma models, and intrinsic resistance to MAPK inhibition was observed in the MDA-MB-231 breast cancer model, whereas the 22rv1 prostate cancer cells appeared to develop MAPK-independent mechanisms of resistance.\nAcquired RAS mutations are associated with MAPK reactivation and resistance to BRAF targeted therapy in Class 1 BRAF mutant cancers and promote cell cycle progression (PMID:28951457, Yaeger et al, 2018). Therefore we performed new experiments (RAS-GTP assays) to evaluate RAS activity levels across all four Class 2 cell lines. As expected, the 2 RAS mutant cell lines (HMV-II and MDA-MB-231) had substantially higher baseline levels of RAS-GTP vs. the RAS WT Class 2 cell lines (FM95 and 22rv1) (new Figure S6a). Next, we compared RAS-GTP levels in resistant vs. parental cells. We found that RAS-GTP levels were significantly higher in resistant vs. parental FM95 cells (new Figure 2d, new Figure S6b), whereas there was no consistent increase in RAS-GTP levels in resistant vs. parental HMV-II, MDA-MB-231 or 22rv1 cells (new Figure 2d, new Figure S6b). We then investigated the immediate down-stream impact of RAS-activity on the MAPK pathway (RAF dimerization) by performing co-immunoprecipitation experiments in our panel of parental & resistant cells. We observed that B+E treatment induce BRAF:CRAF co-immunoprecipitation in both parental and resistant RAS mutant cell lines (MDA-MB-231, HMV-II) and in resistant FM95 cells, but not in 22rv1 cells (new Figure 2e, new Figure S6c).\nIn summary, we observed that increased RAS activity via either intrinsic RAS mutations (MDA-MB-231, HMV-II) or acquired increased wild-type RAS activity (FM95) was associated with high levels of MAPK activity in resistant cells. Whereas in one model (22rv1) we observed no significant increase in RAS activity, RAF co-immunoprecipitation or MAPK reactivation. These results point to RAS activity as a key mediator of resistance in multiple (but not all) Class 2 cell lines.\nTo further interrogate the role of RAS in driving resistance to B+E in Class 2 BRAF mutant cancer cells, we performed functional genomics experiments (2-color CRISPR competition assays). In HMV-II cells, we found that NRAS knockout reduced cell fitness and the fitness of NRAS knockout was further reduced in the presence of B+E (new Figure 2f,g,h). These results implicate mutant NRAS (NRAS Q61K) as a key mediator of intrinsic resistance to B+E in Class 2 HMV-II cells. Importantly, the clinical relevance of these in vitro functional assays were validated by observations from the BEAVER trial. Patient BVR-M-05 had a Class 2 BRAF mutant, RAS wildtype melanoma at baseline, and initially experienced a response to B+E treatment, however at the time of disease progression, this had acquired a new NRAS mutation, implicating mutant NRAS as a mediator of acquired resistance to B+E (new Figure 2h; previously shown in Figure S9a). We also investigated the role of mutant KRAS in MDA-MB-231 cells, and observed similar results. KRAS knockout reduced cellular fitness of MDA-MB-231 cells in the presence or absence of B+E (new Figure S6d,e).\nFinally, the 22rv1 model was unique amongst our Class 2 cells, in that these resistant cells showed no evidence of MAPK reactivation and thus clearly developed MAPK-independent mechanisms of resistance to B+E. 22rv1 cells are castration-resistant prostate cancer cells, and prostate cancers are dependent on androgen receptor (AR) signaling for cellular proliferation. Indeed, 22rv1 cells express an AR alterations (AR-V7 splice variant), which are associated with resistance to androgen receptor pathway inhibitors. Furthermore, AR blockade has been shown to sensitize BRAF V600 mutant melanoma to BRAF/MEK inhibition (PMID: 35705814). Intriguingly, we also found that the “Androgen Response” Hallmark gene-set was also significantly enriched in 22rv1 resistant cells, shown in Reviewer Figure 1. Therefore, we investigated whether AR was implicated in resistance. Indeed, we found that AR knockout reduced cellular fitness in the presence or absence of B+E (new Figure S6f,g). Taken together, these results highlight that some Class 2 BRAF mutant cancers may be able to co-opt MAPK-independent signaling pathways to facilitate cell proliferation in the presence of MAPK inhibitors.\nReviewer Figure 1: AR Response gene set enrichment in the 22rv1 resistant cells. GSEA comparing enriched gene sets in 22rv1 Resistant B+E vs 22rv1 Parental B+E (p = 0.017, FDR = 0.046).\nIn summary, we have performed a series of extensive experiments to characterize key upstream mediators of resistance to B+E in Class 2 BRAF mutant cells. We find MAPK pathway reactivation to be a common - but not universal - feature of resistance. Our results implicate KRAS G13D, NRAS Q61K and AR as some of the key mediators of cell proliferation in Class 2 cells. We believe the characterization of these disparate mediators across models strengthens the rationale to target a protein that functions downstream of these multiple activated pathway (ie. CDK4/6).\nR1-3. In Figures 4h-j, Palbo treatment of all three cell lines demonstrated strong activation of ERK. Could you further elucidate the possible underlying mechanism?\nWe thank the reviewer for asking this question, we also found this to be an interesting observation that we hadn’t previously explored in depth. To address this reviewers question, we performed some additional experiments. First, we evaluated whether a CDK4/6 knockdown via siRNA also induces the increase in pERK as seen with Palbo treatment and we did not observe an increase in pERK. Included here as Reviewer Figure 2 is a representative western blot of this data and the quantified ERK phosphorylation of 3 biological experiments. This was assessed in the MDA-MB-231 parental cell line. We did not observe an increase in pERK when CDK4/6 were knocked down.\nReviewer Figure 2: CDK4/6 kd decreases ERK phosphorylation. A) CDK4 and CDK6 were knocked down in MDA-MB-231 parental cells and ERK phosphorylation was assessed at endpoint. B) Quantified ERK phosphorylation from 3 replicates. Paired t-test *P< 0.05.\nNext, we performed a time-course and a dose escalation of palbociclib in our MDA-MB-231 cells (Reviewer Figure 3). We observed that treatment of Palbociclib increases ERK phosphorylation in a time and dose-dependent manner. We observed a similar dose-dependent increase in pERK after 72hrs treatment with palbociclib in FM95 cells. Additionally, we saw an increase in pERK as the Palbo concentration increased and led to on-target effects (reduction of pRB). These results are shown below in Reviewer Figure 3. This phenomenon has also been observed by other groups (PMID: 36346366, Goodwin et al., 2023. Figure 1G).\nReviewer Figure 3: Palbociclib induces ERK phosphorylation. A) MDA-MB-231 Parental cells were treated with 100nM Palbociclib for 2, 24, 48, and 72 hours (N=3). Representative WB shown below. B) MDA-MB-231 and C) FM95 Parental cells were treated with increasing doses of Palbociclib for 72 hours (N=3). Representative WB shown below. Paired t-test * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001.\nNext, we interrogated whether palbociclib-mediated MAPK activation was potentially mediated by increased RAS activity. However, we did not observe any change in RAS activity levels with palbociclib treatment (Reviewer Figure 4).\nReviewer Figure 4: Palbo Treatment does not alter active RAS levels.\nMDA-MB-231 Parental cells were treated with 100nM Palbociclib,\nBini+Enco (60nM+300nM respectively), and Bini+Enco+Palbo (60nM +\n300nM + 100nM respectively), or DMSO for 72 hours and active RAS\nlevels were evaluated with the RAS-GTP assay.\nTaken together, these results suggest that palbociclib increases MAPK activity, and this may be due to off-target effects of the drug – as we did not observe a similar phenotype when we knocked down CDK4/6 in these cells. Thus, while we agree with the reviewer that this is an interesting and reproducible observation, we believe it will take a substantial amount of investigation to definitively characterize the detailed mechanism by which palbociclib enhances MAPK activity, and that these investigations are beyond the scope of this manuscript.\nR1 - 4. In Figures 6a-b, the in vitro experiments are conducted using four cancer types classified as class 3. However, in Figures 6c-f, the efficacy of the combination B+E+T is validated only in PDX models of colorectal cancer. Does B+E+T also show efficacy in other cancer types?\nWe thank the reviewer for this comment. First, we wanted to clarify one point. In the previous iteration of this manuscript we did indeed evaluate the B+E+T combination in non-colorectal cancer PDX models. The BVR-O-10 PDX model (Figure 6c) is a cholangiocarcinoma model, and B+E+T promoted tumor regression in this model. For the generation of PDX models from patients enrolled on the BEAVER trial, our study was constrained by the limited number of patients with non-colorectal Class 3 BRAF mutations and the engraftment potential of patient-derived samples. However, we recognize the reviewer’s concern of validating this combination in other cancer types. Therefore, we sought to evaluate the combination B+E+T in two 3D spheroid models of melanoma (new Figure S12c-e). 3D spheroid models are considered more physiologically relevant models for in vitro drug screening than traditional 2D cultures as they display various in vivo characteristics including cell-cell interactions, hypoxia, drug penetration, response, and resistance. By shifting growth dependence away from unrestrained proliferation and recapitulating tissue architecture, 3D culture models are useful to bridge the gap between traditional 2D in vitro models and animal studies, making them a valuable tool for drug screening (PMID: 26752500).\nEvaluation of the combination B+E+T in two 3D spheroid melanoma models (WM3629, WM3670) has been included as Supplemental Fig S12c,d,e. In these melanoma spheroid models, we observed a significant decrease in cell viability and a reduction in spheroid size with B+E+T vs. B+E. Taken together, we believe we have provided adequate evidence that this combination is effective in multiple cancer types in vitro (colorectal, lung, melanoma; Figure 6a, Figure S12c,d,e) and in vivo (colorectal cancer, cholangiocarcinoma; Figure 6c,d,e,f).\nR1 - 5. Figure and subfigure titles could be more conclusive than descriptive to enhance readability. For example, instead of a descriptive title, consider: 'Fig. 1: BEAVER trial demonstrates 14% clinical efficacy of Binimetinib and Encorafenib in patients with non-V600E BRAF mutations.'\nPer the editors request, we did not make this change.\nReviewer #2 (Remarks to the Author):\nIn the era of precision oncology, identification of novel combinatory strategies in molecularly selection population is one of the main fields of research. In this scenario, the identification of potential treatment strategies for patients with BRAF class 2 and class 3 alterations is under clinical evaluation. Here Rose and colleagues presented the results of the BEAVER clinical trial, exploring the combination of binimetinib and encorafneib in a cohort of patients with different tumor types harboring BRAF alterations other than class 1. While the experimental strategy has shown limited activity, the authors conducted multiple exploratory analysis to identify potential mechanisms of resistance and potential therapies. I would like to congratulate with the authors for their excellent manuscript!\nWe would like to thank this reviewer for their time in reviewing this manuscript and we are very appreciative of their congratulations. We have responded to their comments below.\nI have only few comments:\n- It could be of interest if the authors present data regarding the previous line of treatment since a target therapy could have an impact on the baseline molecular profile.\nWe have updated Supplemental Table S3 to include information on prior therapies.\n- Are there any information’s regarding subsequent therapies?\nUnfortunately we did not collect information on subsequent therapies. However we do note that these are heavily pre-treated patients, and the median OS of the entire cohort was only 6.2 months (Figure S1). Therefore we suspect that many patients did not receive subsequent therapies.\nReviewer #3 (Remarks to the Author):\nThis manuscript by Rose et al is primarily focused on the study of non-V600 BRAF mutant tumors to understand molecular mechanisms of treatment response and resistance to guide the design of more effective therapies. The authors provide data from a Phase II clinical trial designed to evaluate activity of binimetinib and encorafenib targeting MEK and BRAF, respectively, to treat non-V600 BRAF mutant disease across a wide array of tumor histologies. PDX models were established from tumor biopsies to facilitate the identification of mechanisms of resistance. Two separate lines of investigation were pursued to generate preclinical data in support of combination of binimetinib and encorafenib with a third agent to improve response. First, combination of MEK/BRAF inhibitors with a CDK4/6 inhibitor to target cell cycle progression in Class 2 BRAF mutant tumors, and second, combination of these agents with a SHP2 inhibitor to address co-occurring activating mutations in RAF1 or NRAS in Class 3 tumors were explored.\nThis study addresses an area of high unmet medical need since the mean overall response irrespective of class of non-V600 BRAF mutation across the entire cohort of Phase 2 patients treated with binimetinib plus encorafenib was ~6 months. Overall, the data and studies reported here lend further support for key similarities and differences between class 2 and class 3 BRAF mutants for the design of improved precision therapies. However, it is recommended that the following deficiencies be addressed to strengthen the overall impact of this study.\nR3-1. The Phase 1/2 study published by Yaeger and colleagues, who also evaluated clinical activity of binimetinib/encorafenib against non-V600 BRAF tumors (Rustgi et al; Oncologist 2023), should be cited. Their findings appear to corroborate the findings of the present study.\nWe thank the reviewer for calling attention to this reference, we have now included a reference to this manuscript in the 2nd paragraph of the discussion section: “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 response.33”\nR3-2. Was any biopsy analysis conducted showing incomplete inhibition of ERK signaling? Since biopsies were obtained during the clinical trial, it is unclear why this was not explored, as biopsy tissue is more clinically relevant than xenografts established from a heterogeneous specimen.\nWe thank the reviewer for this question. We did indeed perform pre and on-treatment biopsies in some patients. However, per protocol, on-treatment biopsies were optional, and thus only a minority of patients consented to this procedure. To address this reviewer comment, we reviewed all paired pre-treatment/on-treatment biopsies, performed H&E, pERK and ERK IHC. H&E sections were interrogated by a pathologist (co-author, Dr. Zaid Saeed Kamil) to confirm adequate tumor tissue. After review, we had paired biopsies with adequate tumor tissue available from 6 patients (BVR-M-04, BVR-O-10/12/13/15/17). Unfortunately 5/6 patients had PD as best response and one patient (BVR-O-10) had SD as best response. Thus we are not able to make any associations between changes in pERK levels and clinical response. Amongst these 6 non-responders however, we did observe a significant decrease in pERK+ cells in on-treatment biopsy samples compared to pre-treatment biopsy samples. These results are included in the revised manuscript as new Figure S3d. These data confirm that we did indeed observe on-target MAPK pathway inhibition with these inhibitors in patients with Class 2 & 3 non-V600 BRAF mutations. Anecdotally (and intriguingly), we did observe higher baseline pERK levels in the tumor types that were associated with higher response rates (melanoma, biliary tract tumors, BVR-M-04/BVR-O-10; average 13.4% pERK positive cells at baseline) compared to colorectal tumors (BVR-O-12/13/15/17; average 1.7% pERK positive cells at baseline; P=0.0002 t-test). This observation indicating lower baseline MAPK activity in tumor types that are less responsive to MAPKi could in-part explain a lack of benefit from MAPK inhibitors in colorectal tumors; however, our sample size is too small to make any definitive claims in this regard.\n3. Figure 1b: since BRAF/MEK inhibitor combination therapy has been reported to elicit activity against BRAF V600 mutated cholangiocarcinomas, they should be distinguished from pancreatic carcinomas as opposed to all being classified as pancreaticobiliary tumors.\nPer this reviewers suggestion we have removed the patient with pancreatic cancer from the “pancreaticobiliary” tumor grouping. This patient is now included in the “other” tumor group. The updated tumor groupings are: melanoma, colorectal cancer, biliary tract tumors, and other. We have revised Figure 1b,c,e; Figure S3; and Table S1 to reflect these changes. Biliary tract cancers are a heterogenous group of tumors that include cholangiocarcinomas, gallbladder and ampullary cancers (PMID: 35032765, 37416744, 38266541, 34616513, 34300274, 24857067). Importantly, BRAF/MEK inhibitors have demonstrated efficacy in biliary tract cancers with BRAF V600E mutations (PMID: 37059834).\n4. Figure 2b: Tumor growth inhibition (TGI) needs to be defined.\nWe determined the tumor growth inhibition (TGI) index to compare the relative tumor growth inhibition Bini+Enco treatment vs. vehicle treatment in corresponding PDXs. TGI was calculated with the following equation: 100 –(100*(AUCB+E/AUCvehicle)), where the AUC was the calculated average area under the curve of all individual tumor growth curves within that treatment group. We have updated the figure legend for Figure S4c to include this definition.\n4. Figure 2b: The text states that nine PDX models were established, but there are only eight models plotted here. The conclusion that there is an indirect correlation between clinical change in target lesion vs preclinical efficacy of the PDX model established from that lesion does not appear to be justified. Five of the eight PDX models shown in this figure originated from colorectal tumors, which were the most refractory histology evaluated (5 of 6 showing clinical progression).\nWe did establish 9 PDX models, from BEAVER trial patients. We performed targeted panel sequencing on biopsy samples from all 9 patients and whole exome sequencing on all PDXs. We were unable to detect a BRAF mutation in one of the 9 PDX models (BVR-M-04) that we developed (Figure S4b). The BRAF mutation that was indicated on this patient’s clinical pathology report (BRAF G466E) was also not present on the patient biopsy sample when we evaluated it with the TSO500 NGS assay; however, several other gene alterations (NF1, TP53, POLE, RB1) were present in both the patient sample and the PDX sample (Figure 1c, Figure S4b). This suggests that this patient had a sub-clonal BRAF mutation detected on the clinical sample that was used to determine trial eligibility. Therefore, because this was a BRAF WT PDX, we did not test the efficacy of B+E, and we have only plotted 8 models in this graph (now Figure S4d).\nWe feel strongly that our conclusion that “there is an indirect correlation between the clinical change in target lesion size and the tumor growth inhibition index of the corresponding PDX” is indeed justified based on the data as presented. We do not claim that we can predict response based on this model (as we unfortunately did not develop any PDXs from patients who experienced response). We claim only that that there is inverse correlation in patient tumor growth as measured by an established standardized metric (RECIST 1.1) and PDX tumor growth inhibition with the same drug treatment. The purpose of this is analysis is to confirm that the PDXs are useful models (albeit imperfect, as are all models) for drug treatment experiments and that responses in the PDX are associated with responses in patients for the same drug treatment. This is a well-established and uncontroversial feature/benefit of using PDX models. With all that being said, we have de-emphasized this result in the main manuscript text and moved this figure from Figure 2 to Supplemental Figure 4.\n5. Figure 3g: Were PDX models not available from patients testing positive for E2F/G2M gene alteration to evaluate in vivo efficacy of binimetinib/encorafenib/palbociclib?\nWe thank the reviewer for this question. The following BEAVER PDX models had alterations in E2F/G2M genes: BVR-M-02 (PMS2, TP53); BVR-O-13 (TP53); BVR-O-15 (TP53); BVR-O-17 (SMAD3). Of these models, we evaluated the binimetinib/encorafenib/palbociclib combination in the Class 2 BRAF mutant PDX: BVR-O-17 (Figure 4g), and in Class 3 BRAF mutant PDX: BVR-O-13 (Figure S8) PDXs, in vivo.\n6. Figure 3i: Combined knockdown of CDK4 and CDK6 does not appear to potentiate cell growth inhibition mediated by binimetinib and encorafenib at the low doses (line 360).\nWe thank the reviewer for this comment, we agree that CDK4/6 knockdown did not potentiate cell growth inhibition mediated by low dose Bini+Enco, but CDK4/6 knockdown did potentiate cell growth inhibition mediated by high dose Bini+Enco . We have adjusted the text to more accurately reflect this observation: “Notably, the combined knockdown further potentiated cell growth inhibition mediated by B+E at high doses (Figure 3i).” (page 9)\n7. Triple therapy combination (B+E+palbociclib) does not appear to be more effective than palbociclib alone against MB-231 cells (lines 367-369).\nIn response to this comment, we have updated Figure 4A. Indeed, at the 100nM palbociclib dose, the triple therapy was not more effective than palbociclib alone in the MDA-MB-231 cells. Multiple clonogenic experiments were performed with 25nM, 50nM and 100nM palbociclib doses and differing doses of B+E. We have updated Figure 4A with a higher Bini+Enco dose which inhibits proliferation of these cells (1000nM Enco + 200nM Bini) as well as a palbociclib dose that is effective alone (50nM). The triple therapy combination is more effective than Bini+Enco (p=0.0129) and Palbociclib alone (p < 0.0001). We limit our description of the results to the comparison between B+E and the triple therapy.\n8. Figure 4e-g: added benefit of combining three agents (B+E+palbociclib) does not appear to be significantly enhanced compared to palbociclib alone. Statistics should be provided comparing those two arms in each study.\nWe thank the reviewer for raising this point. As all of these PDXs were derived from patients who were non-responders to bini+enco, therefore, we did not power each individual PDX study to have a sample size that would allow us to detect statistically significant differences between B+E+P and B+E or Palbo. The experiments were powered to detect statistically significant differences between B+E+P and vehicle treatments.\nHowever, to address this reviewer’s concern, we have now compiled the results of all of the colorectal PDX models that we used to evaluate this regimen (BVR-O-04, BVR-O-12, BVR-O-13 and BVR-O-17). Here we reported the change in tumor volume and perform multiple comparison testing to evaluate the difference between B+E+P vs. vehicle (P<0.0001); B+E+P vs. B+E (P<0.01), and B+E+P vs. Palbo (P<0.05). This data is now included in the revised manuscript as Figure S8d. Additionally, we performed a similar type of analysis to compare the B+E+TNO155 tumor growth to vehicle (P<0.001), B+E (P<0.01) or TNO155 (P<0.05). This analysis is also now included in the revised manuscript as Figure S12f.\n8. Figure 4e-g: The absence of meaningful tumor regressions in the triple combination arms is a concern and weakens the conclusion that the preclinical data support clinical evaluation of this combination strategy for Class 2 & 3 BRAF mutant cancers (lines 538-539).\nWe acknowledge that this triple therapy regimen (B+E+palbo) delayed tumor growth in all tested PDXs, but it did not elicit tumor regressions in most of these models. We did however, observe tumor regressions with B+E+palbo triple therapy in the BVR-O-10 cholangiocarcinoma model (Figure S8b). We do not agree that this finding weakens the conclusion that these preclinical data support the clinical evaluation of this combination strategy. It is important to note that many of our PDX models are derived from patients with metastatic colorectal cancer (mCRC) that were refractory to multiple chemotherapies (Table S3). In clinical trials evaluating new treatments for chemotherapy-refractory mCRC, partial responses (tumor regressions) are rare. Indeed, the response rate for the BEACON regimen (encorafenib and cetuximab) in BRAF V600E mCRC is only 21%, and despite this low response rate, this regimen provided an overall survival benefit and is a standard-of-care treatment for mCRC (PMID: 31566309). Similar results were also observed for other recently approved treatments for chemotherapy refractory mCRC. Indeed, the objective response rate for Fruquintinib was <2% (7/461 pts responded) in a randomized Phase 3 clinical trial that also demonstrated an overall survival benefit, resulting in its FDA approval for treatment refractory mCRC (PMID: 37331369). Thus, we believe that significant delays in tumor growth in PDXs provides sufficient rationale to support further clinical investigation of this regimen.\n9. The weight loss seen in the triple combination studies with B+E+SHP2 inhibitor highlights the importance of carrying out preclinical evaluation of therapeutic index of novel treatment strategies, an important concept that has not been addressed anywhere in the manuscript.\nWe thank the reviewer for this comment and we agree that this finding supports the need for preclinical studies to define potentially effective novel drug combinations prior to clinical development. However, preclinical mouse models have limited ability to predict clinical toxicities of the same regimens (PMID: 31998852). Moreover, many clinical toxicities can be mitigated with supportive medications that are not typically used in preclinical studies designed to evaluate drug efficacy. For example, we observed weight loss in these mice treated with B+E+TNO155. In the clinic, weight-loss can often result from medication-induced nausea, which can be easily managed with supportive anti-emetic medications. Thus, we do not believe that the weight loss observed in these animal studies precludes clinical evaluation of this combination. However dose-finding studies testing different treatment doses and schedules would be required in any Phase 1 study evaluating this regimen, as is typically done in early drug development studies.\nReviewer #4 (Remarks to the Author):\nThis is a biostatistical review focused on the clinical trials component of the article.\nIn this work, the authors report the outcomes from the BEAVER clinical trial, a single-arm phase 2 trial of BRAF and MEK inhibitors in patients with non-V600E BRAF mutations. Due to challenges in accrual, the study was unfortunately not able to complete the planned accrual, nor did the investigators observe 4 responses to meet the design-specified efficacy threshold. I applaud the authors' careful interpretations of the primary clinical findings, and a strength of this article is the follow-up lab-based research using patient samples from the clinical trial.\nI have some revision ideas that I hope help can help a reader of this article:\nR4 - 1. In the 1st Discussion paragraph summarizing the results, the authors could consider repeating that there was reduced accrual to remind the reader and contextualize the failure to meet the design-specified efficacy threshold. I liked the language in the limitations section that, because the planned enrollment was larger than the actual enrollment, it is hard to interpret the fact that only 3 responses were observed. Maybe a sentence like the following could fit into lines 487-489:\n\"We did not meet the planned enrollment total due to accrual challenges, and we also did not observe the number of responses necessary to meet the design efficacy threshold; therefore, we cannot make any definitive statement regarding the efficacy of this regimen in this patient population.\" (If something like this is used, please also review the limitations paragraph for any redundancy with this language.) Please also consider a similar type of statement for the abstract, where the primary efficacy conclusion (or lack thereof) did not appear to be mentioned.\nWe thank the reviewer for their positive evaluation of this manuscript, and for this suggestion. We have added the following sentence to the first paragraph of the discussion section:\n“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.”\nAs recommended by the reviewer, to reduce redundancy, we have removed the following sentence from the limitations section: “We cannot make any definitive claims about the efficacy of this regimen within specific cancer types because our sample size was too small.”\nR4 -2. Related to above, the authors could consider including a reminder in the Discussion that patients on this study experienced only expected toxicities from this regimen.\nWe have added the following statement to the opening paragraph of the Discussion: “We did, however, confirm the established safety profile of this therapeutic regimen”\nMinor comments:\nR4 - 3. Could the authors clarify that all (13) patients who had pre-treatment biopsy contributed to the attempted PDX models? (If accurate?) In lines 272-273, it was unclear if there was a specific criterion for attempting the PDX or just that all available samples were used.\nWe attempted to develop PDX from all patients who had a fresh tumor biopsy with sufficient tissue cores available for implantation. However, fresh tumor biopsies were optional unless the patient did not have suitable archival tissue available. Specific detailed information describing whether a pre/on-treatment biopsy was performed and if a PDX was attempted and/or established from these biopsies is already included in Table S3. In summary, 13 patients underwent a pre-treatment and/or on-treatment biopsy, we did attempt to develop a PDX from all 13 patients, and we successfully established PDXs from 9/13 patients.\nR4-4. I noticed the p-value stated on Figure S3B slightly differs from line 248.\nThank you to this reviewer for their close attention to detail. We have made revisions to Figure S3B, in response to the comments from Reviewer #3. There is a new p-value for Figure S3b (P=0.016) based on the modifications to the groups, this has been updated in the Figure and in the corresponding text of the manuscript.\nAdditional Reviewer Question\nWhen checking your sample size calculation using an online calculators (e.g. https://trialdesign.org/one-page-shell.html#Simon2S) and the parameters on lines 604-605 (P0 = 0.05, P1 = 0.25, Alpha = 0.05, Power = 0.80), their results did not match your calculations.\nThe original sample size calculations for this study were made using that statistical program called PASS. While these original calculations were made in 2017 using an earlier version of the software, we have repeated these calculations using the more recent version of the software (PASS 2023, v23.0.2) and included a print out of the results from those analyses that support our original calculations:\nFINAL REMARKS\nWe would again like to thank all of the reviewers and the editorial team for their careful review of this manuscript including their many helpful suggestions. We started working on this project in 2017 when we began writing the clinical trial protocol. The work described in this manuscript thus encompasses 8 years of rigorous, multidisciplinary, multi-institutional collaborative work. It includes a clinical trial, and extensive in vitro and in vivo experimentations including key functional genomics experiments. Importantly – we identified potential mechanisms of resistance in patient samples (ie. acquisition of new NRAS mutations in patients progressing on treatment with B+E) and validated – using CRISPR competition assays - that mutant NRAS mediates resistance to B+E in Class 2 BRAF mutant cancer cells. Based on these observations, we also extensively evaluated two new treatment strategies that could overcome B+E resistance that we assessed in a wide array of in vitro and in vivo models. We are very proud of the quality and potential impact of this work. We hope that the reviewers and the editorial team will agree with us and consider this substantially revised manuscript acceptable for publication in Nature Communications."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I thank the authors for their detailed and thoughtful responses, which have substantially strengthened the manuscript. I appreciate the clarification on the tumor-type agnostic role of oncogenic BRAF and the rationale for examining multiple cancer types, which is well supported by clinical data and references. The additional experiments identifying upstream mediators of resistance and the 3D spheroid data broaden the translational relevance of the findings. The follow-up analysis of ERK activation with palbociclib is thorough. Overall, the revised manuscript provides a more comprehensive and convincing framework for understanding resistance mechanisms and therapeutic strategies in Class 2 and 3 BRAF mutant cancers.", "reviewer_2": "The comments has bene addressed.", "reviewer_3": "Rose and colleagues have been highly responsive to questions raised in the previous review and the revisions have significantly strengthened the manuscript. It is important that the results of the Phase II BEAVER clinical trial be published. The authors' highly translational studies to understand mechanisms of resistance further bolster the impact of this work.", "reviewer_4": "Thank you for your hard work on these revisions. I only have one remaining comment regarding the biostatistics/clinical trial component of the article: regarding the sample size calculation, did the team assess designs with sample sizes smaller than 26? It appears that the PASS screenshot searched for designs in the range 26-33. PASS is not this reviewer's typical sample size software, but other software like EAST and trialdesign.org appear to have found designs with smaller sample sizes using P0 = 0.05, P1 = 0.25, (one-sided) Alpha = 0.05, Power = 0.80 (Simon's minimax maximum sample size=16 and optimal=17)."}, "author_response": "The reviewers comments are included below in blue text. We would sincerely like to thank all reviewers for their careful review of the manuscript, their important critiques and suggestions, and their positive appraisal of our revised manuscript. For the one remaining comment from Reviewer 4, we have revised the manuscript to include the min-max sample size specifications used, and we will consider the alternative recommended software for future trial designs."}]

Decision letter