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To identify genes whose upregulation confers resistance to MAPK pathway inhibition, we expressed 15,906 human open reading frames (ORFs)5 (Extended Data Fig. 1) in a BRAF(V600E)-mutant, MAPK-pathway-dependent melanoma cell line (A375)6,7 and determined their effects on sensitivity to small-molecule inhibitors targeting RAF, MEK, ERK8 or a combination of RAF and MEK (Fig. 1a). In this experiment, 14,457 genes (90.9%, Fig. 1a) passed quality-control filters and were evaluated for their effects on drug sensitivity (Extended Data Fig. 2a, b, c). We identified 169 genes (1.16%) whose overexpression conferred resistance to at least one MAPK-pathway inhibitor (Extended Data Fig. 2d).

Figure 1: Near-genome-scale functional rescue screens for resistance to RAF, MEK and ERK inhibitors.
figure 1

a, A375 cells transduced with the lentiviral expression library (n = 2 technical replicates) were treated with indicated inhibitors and assayed for viability in the presence of compound alone (x axis) and viability in compound relative to DMSO (y axis). Values are presented as a z score. Genes (n = 169) with z scores ≥2.5 (green dashed line) were nominated as candidate resistance genes. b, Summary of protein classes of candidate genes identified in primary drug resistance screens. Only protein classes containing ≥2 genes are shown. GAP, GTP-activating protein; GEF, guanine nucleotide exchange factor; PTK, protein tyrosine kinase; RTK, receptor tyrosine kinase; TK, tyrosine kinase.

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These screens identified diverse resistance effectors (Fig. 1b), including genes that activate ERK signalling (KRAS(Gly12Val), MEK1(Ser218/222Asp), RAF1, MOS, FGR, AXL, FGFR2, SRC and MAP3K8 (also known by its protein abbreviation COT))6,9,10,11,12,13 and RAS–guanine exchange factors (RASGRP2, RASGRP3 and RASGRP4) (Extended Data Fig. 2d). Previously unrecognized resistance mechanisms were also identified, including modifiers of ‘stem-ness’ (OCT4 (also known as POU5F1), NANOG), ubiquitin pathway components (KLHL-family members, TRIM-family members) and non-Ras guanine exchange factors (VAV1, other DBS and PLEKHG family members). Furthermore, several ERK-regulated transcription factors emerged, including FOS, JUNB, ETS2 and ETV1 (Extended Data Fig. 2d).

To verify resistance effects, we re-expressed each candidate gene in A375 cells and calculated the area under the curve (AUC, Extended Data Fig. 3b) for MAPK-inhibitor growth inhibition (GI50; concentration that inhibits growth by 50%) assays (Extended Data Fig. 3a). The fraction of candidate genes that were validated (P < 0.05) by these experiments ranged from 64.2% (RAF inhibitors) to 84.5% (RAF–MEK inhibitors) (Fig. 2a). Of the 75 RAF-inhibitor resistance genes, 71 (94.6%) also imparted resistance to MEK inhibitors and RAF–MEK inhibitors and only 18 (25.4%) of the 71 RAF-, MEK- and RAF–MEK-inhibitor resistance genes retained sensitivity to ERK inhibitors (Extended Data Fig. 3d, e). Thus, the majority of the genes that confer resistance to single agent RAF inhibitors are resistant to both RAF–MEK inhibitors (94.6%) and ERK inhibitors (70.6%) (Extended Data Fig. 3e, f). Aside from a subset of MAPKs and tyrosine kinases, most genes produced only minimal phosphorylated-ERK rescue in the presence of MAPK inhibitors (Extended Data Fig. 3c), consistent with the high degree of ERK-inhibitor resistance observed in our validation experiments (Fig. 2a). These data suggest that many resistance mechanisms may circumvent the entire RAF–MEK–ERK module.

Figure 2: Candidate resistance genes segregate into validating protein classes.
figure 2

a, Area under the curve (AUC) was calculated for MAPK-inhibitor drug sensitivity curves in A375 expressing candidate and control genes. Data are presented as a z score (y axis), relative to the AUC of all control genes across each MAPK inhibitor. b, Seven BRAF(V600E)-malignant melanoma cell lines expressing the indicated candidate or control genes were assayed (n = 2 technical replicates) for viability after treatment with indicated MAPK inhibitors. Cellular viability is presented as a z score relative to control genes. Genes with a z score ≥4 in ≥2 conditions (drug or cell line) are shown. c, The strength of the resistance phenotype for each candidate and control gene across all MAPK inhibitors and cell lines is quantified and presented as a composite rescue score.

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We extended our validation studies across seven additional BRAF(V600E) lines (Extended Data Fig. 4a–d). Overall, 110 genes (66.7%) conferred resistance to the query inhibitors in at least two of seven additional BRAF(V600E) melanoma lines (Fig. 2b). Many genes again conferred resistance to all inhibitors or combinations of inhibitors examined (Fig. 2b). Next, we organized resistance genes into mechanistically related classes and identified those that exhibited the most extensive validation across our BRAF(V600E) cell lines (Fig. 2c). Based on these criteria, G-protein-coupled receptors (GPCRs) emerged as the top-ranked protein class (Extended Data Fig. 4e). Each validated GPCR conferred resistance to all MAPK inhibitors tested (Fig. 2b). Many GPCRs activate adenyl cyclase, which converts ATP (ATP) to cAMP14, the primary target of which is protein kinase A (PKA). Consistent with these observations, the adenyl cyclase gene ADCY9 was also identified as a resistance effector (Extended Data Fig. 2d and Extended Data Fig. 4f, g) and the catalytic subunit of PKAα (PRKACA) had the highest composite rescue score within the serine/threonine kinase class (Fig. 2b, c). Both genes conferred resistance across all MAPK-pathway inhibitors examined (Fig. 2b and Extended Data Fig. 4f).

We therefore reasoned that a signalling network characterized by GPCR activation and induction of adenyl cyclase–cAMP–PKA may induce resistance to MAPK inhibitors in melanoma. This predicted network resembles a growth-essential lineage pathway in primary melanocytes, which require GPCR-mediated cAMP signalling for growth in vivo15. To test this hypothesis, we determined whether cAMP-mediated signalling was sufficient to confer resistance to MAP kinase pathway inhibitors. Both cAMP and the adenyl cyclase activator forskolin increased intracellular cAMP (Extended Data Fig. 5a) and conferred resistance to all MAPK-pathway inhibitors queried across a panel of cell lines (Fig. 3a and Extended Data Fig. 5b) without affecting baseline cell growth (Extended Data Fig. 5c, d). Forskolin and cAMP resistance was PKA-dependent; it was blocked using the PKA inhibitor H89 (Fig. 3b and Extended Data Fig. 5e). The resistance phenotype was also relatively specific to MAPK-pathway inhibitors (Extended Data Fig. 5f). Thus, cAMP and PKA activation can confer resistance to MAPK-pathway inhibition in melanoma cells.

Figure 3: A cyclic AMP signalling network mediates resistance to RAF, MEK and ERK inhibitors.
figure 3

a, Average fold change (relative to DMSO) in MAPK inhibitor GI50 or AUC in a panel of BRAF(V600E)-mutant cell lines treated with vehicle (DMSO), forskolin and IBMX (FSK/I) or dibutyryl cAMP and IBMX (cAMP/I). n = 8 technical replicates, representative of 3 independent experiments. b, Heat map showing relative cell viability (per cent of DMSO) following treatment with FSK/I or cAMP/I in the presence of vehicle (DMSO), the PKA inhibitor H89 and a single dose of indicated MAPK inhibitors. c, Immunoblot analysis of phosphorylated CREB/ATF1 (Ser 133/Ser 63) in lysates from WM266.4 virally transduced with the indicated expression constructs. d, Viability of WM266.4 expressing either LacZ (control) or dominant-negative CREB alleles (CREBR301L or A-CREB) following treatment with forskolin and IBMX (FSK/I) in the presence of indicated MAPK inhibitors. Viability is expressed as a percentage of DMSO. Error bars represent s.d. of mean, n = 6 technical replicates, representative of 2 independent experiments. e, Quantification of phosphorylated CREB (pCREB) and pATF1 expression following immunoblot analysis of lysates extracted from BRAF(V600E)-mutant human tumours. Tumours were biopsied pre-initiation of treatment (n = 5), following 10–14 days of MAPK-inhibitor treatment (on-treatment, n = 6) or following relapse (n = 7). MAPK-inhibitor therapy is noted. All available samples were tested and reported. Pre-treatment and on-treatment samples are paired. *P < 0.05, one-tailed t-test on treatment cohorts, which may not directly inform responses in individual patient samples. f, Immunoblot analysis of lysates from WM266.4 following treatment with FSK/I or cAMP/I in the presence of indicated MAPK inhibitors. Quantification of pCREB relative to CREB is shown.

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Two well-characterized transcription factor substrates of cAMP and PKA are CREB and ATF1, which regulate the expression of genes whose promoters harbour cyclic AMP response elements (CREs). To determine whether cAMP-mediated resistance may involve a CREB-dependent mechanism, we measured phosphorylation of these proteins following addition of either forskolin or exogenous cAMP. Both agents (Extended Data Fig. 6a, b), as well as most GPCR genes (Fig. 3c and Extended Data Fig. 6c, d), induced CREB and ATF1 phosphorylation, although only a subset of GPCRs increased steady-state intracellular cAMP (Extended Data Fig. 6e). Expression of dominant-negative CREB proteins (CREBR301L(ref. 16) or A-CREB17; Extended Data Fig. 6f) suppressed forskolin-induced resistance to all MAPK-pathway inhibitors tested (Fig. 3d). These results support the hypothesis that cAMP-mediated resistance may operate in large part through a CREB-dependent mechanism, though the roles of other downstream effectors cannot be excluded.

We next assessed the possible contribution of a cAMP–PKA–CREB mechanism in BRAF(V600E) melanoma patients by measuring CREB and ATF1 phosphorylation in tumour biopsies obtained before or during treatment and following relapse with vemurafenib alone or dabrafenib and tremetinib in combination (Extended Data Fig. 7a). In contrast to cell lines in vitro, CREB and ATF1 phosphorylation was detectable in pre-treatment BRAF(V600E) melanoma biopsy specimens (Fig. 3e and Extended Data Fig. 7b). These results were consistent with the fact that cAMP pathway agonists are excluded from melanoma tissue culture media in vitro. Levels of phosphorylated CREB and ATF1 were suppressed in the cohort of patients treated with RAF or RAF–MEK inhibition (Fig. 3e and Extended Data Fig. 7b, c, d). In contrast, the levels of CREB and ATF1 phosphorylation observed in patient cohorts upon tumour relapse were statistically indistinguishable from those detected in the pre-treatment cohort (Fig. 3e). However, in the single case in which matched pre-, on-treatment and post-relapsed samples could be assessed, levels of phosphorylated CREB and ATF1 did not correlate with drug response (Extended Data Fig. 7b). These preliminary clinical results thus raise the possibility that a CREB-dependent mechanism might contribute to resistance to RAF–MEK inhibition in a subset of melanomas.

Based on these clinical findings, we sought to determine whether MAPK-pathway inhibitors might modulate levels of phosphorylated CREB and ATF1 in vitro when cAMP-dependent signalling is active. We treated BRAF(V600E) melanoma cells with cAMP and IBMX (a non-selective inhibitor) and measured phosphorylation of CREB and ATF1 following exposure to MAPK inhibitors. Each MAPK inhibitor partially blunted the increase in phosphorylated CREB and ATF1 produced by exogenous cAMP (Fig. 3f and Extended Data Fig. 7e), suggesting that cAMP-dependent activity of CREB and ATF1 may be reduced by pharmacologic MAPK inhibition.

In melanocytes, oncogenic BRAF or NRAS can substitute for cAMP signalling18,19,20. We therefore reasoned that a cAMP-mediated lineage program might mediate resistance by inducing CREB-dependent transactivation of effectors normally under MAPK control (Fig. 4f). We identified CREs in the promoters of 19 resistance genes (P = 5.0 × 10−50; Fig. 4a and Extended Data Fig. 8a), of which three lineage-expressed (Extended Data Fig. 8c) transcription factors—MITF, FOS and NR4A2—showed high composite resistance scores (z > 50; Extended Data Fig. 8b). MITF, FOS, NR4A2 and NR4A1 (an NR4A2 homologue and validated resistance gene) showed reduced transcript levels following MEK inhibitor treatment (Fig. 4b). Activating MITF phosphorylation21,22 decreased within 1 h and total MITF protein was undetectable 48–96 h after MEK inhibition (Extended Data Fig. 9a, b). All four transcription factors exhibited 2- to 20-fold increases in messenger RNA expression within 1 h of forskolin treatment (Fig. 4c) and MITF showed sustained increases in protein expression across multiple melanoma cell lines and MAPK pathway inhibitors (Extended Data Fig. 9c–f). Thus, CREB-responsive transcription factor resistance genes operate downstream of both MAPK- and cAMP-dependent signalling.

Figure 4: Candidate resistance genes are transcriptional effectors of the MAPK and cAMP pathways.
figure 4

a, Schematic outlining the identification of candidate resistance genes endogenously regulated by cAMP. b, Quantification of TBP-normalized mRNA levels using real-time quantitative PCR (relative to DMSO treatment) following a time course of MEK inhibitor treatment. Error bars represent s.d. of mean, n = 3 technical replicates representative of 3 independent experiments. c, Quantification of TBP-normalized mRNA levels using real-time quantitative PCR (relative to DMSO-treatment) following treatment with forskolin and IBMX (FSK/I) for the indicated times in the presence of vehicle (DMSO) or MEK inhibitors. Error bars represent s.d. of mean, n = 3 technical replicates representative of 2 independent experiments. d, Immunoblot analysis of lysates from WM266.4 treated with DMSO or MEK inhibitor followed by treatment with panobinostat, vorinostat or entinostat and subsequently stimulated with FSK/I. e, Cellular viability of WM266.4 treated with the indicated combinations of MAPK inhibitors, HDAC inhibitors and FSK/I. Cell viability is shown as a percentage of DMSO in un-stimulated or non-drug-treated cells. Error bars represent s.d. of mean, n = 6 technical replicates representative of 2 independent experiments. f, A mechanistic model of GPCR-mediated resistance. Solid arrows, direct regulation; dotted arrows, indirect regulation.

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To further interrogate connections between cAMP signalling and resistance genes, we employed an expression profiling resource generated by the Library of Integrated Network-based Cellular Signatures (LINCS) program; an extensive catalogue of gene-expression profiles collected from human cells following chemical and genetic perturbation. We compared the signatures derived from all candidate resistance genes to a Library of Integrated Network-Based Cellular Signatures (LINCS) signature of adenyl cyclase stimulation and found that the genes most similar to the signature of adenyl cyclase activation were enriched for GPCR-pathway-associated candidate genes, including GPCRs, PKA and cAMP–MAPK-regulated transcription factors (Extended Data Fig. 9g). Thus, GPCR-pathway-related resistance genes and cAMP agonists function to elaborate a common transcriptional output.

Of the genes co-regulated by MAPK, and cAMP–CREB, MITF was intriguing because of its essential role in melanocyte development23 and as a melanoma ‘lineage survival’ oncogene19. Expression of PKAα, ADCY9 or a subset of resistance-associated GPCRs enabled sustained MITF expression, even in the setting of MEK inhibitors (Extended Data Fig. 9h), thereby confirming that a GPCR–PKA–adenyl cyclase cascade can regulate MITF expression in melanoma cells. Moreover, impairment of MITF protein levels by small hairpin RNA (shRNA) (Extended Data Fig. 10a, b) or co-treatment with a PKA inhibitor (H89, Extended Data Fig. 10c) blunted forskolin-mediated resistance to MAPK-pathway inhibitors (Fig. 3b and Extended Data Fig. 10a).

In a series of three patient-matched melanoma biopsies obtained over the course of RAF–MEK inhibition, we observed that MITF levels were reduced following initiation of MAPK-inhibitor therapy and partially restored in the context of relapse in one patient (Extended Data Fig. 10d), consistent with the idea that aberrant expression of certain cAMP- and PKA-regulated transcription factors may correlate with resistance in some melanoma patients. Collectively, our findings indicate that resistance-associated transcriptional outputs may be governed by several transcription factors in melanoma cells.

Our results support a model in which aberrant signalling from melanocyte lineage pathways may converge on MITF or other transcription factors to drive resistance to MAPK pathway inhibitors. SOX10 and MITF expression can be impaired following treatment with histone deacetylase inhibitors (HDAC inhibitors), although these agents do not act exclusively through SOX10 and MITF24. We reasoned that combined HDAC and MAPK inhibition might prevent cAMP- and MITF-driven resistance in melanoma cells. Indeed, multiple HDAC inhibitors (panobinostat (LBH589), vorinostat (SAHA) and entinostat (MS275)) reduced both SOX10 and MITF expression (Extended Data Fig. 10e), even in the presence of forskolin (Fig. 4d and Extended Data Fig. 10f). Each of these HDAC inhibitors reversed cAMP-mediated resistance to MAPK-pathway inhibition in vitro (Fig. 4e). Of note, forced expression of MITF did not abrogate HDAC-inhibitor sensitivity, indicating that the HDAC-inhibitor growth inhibitory effects do not act solely through this mechanism (Extended Data Fig. 10g). Nevertheless, these results raise the possibility that addition of HDAC inhibitors to combined RAF–MEK inhibition may offer a novel clinical strategy to achieve more durable control of some BRAF(V600E) melanomas.

The clinical benefit of RAF–MEK-inhibitor therapy in BRAF(V600E) melanoma remains temporary, and resistance mechanisms are incompletely understood. The GPCR–cAMP–adenyl cyclase–PKA–CREB module identified here is highly reminiscent of lineage survival signalling in melanocytes. Our results and those of other groups25,26 suggest that this lineage dependency may become reactivated as part of a clinical mechanism of resistance to RAF–MEK inhibition (Fig. 4f) and are bolstered by recent studies showing that MITF transcriptional targets are up regulated during the course of treatment with MAPK-pathway inhibitors27. The application of genome-scale functional approaches to characterize anticancer drug resistance, together with directed experimental and clinical studies, may offer a general framework for discovery and clinical prioritization of novel therapeutic regimens.

Methods Summary

The arrayed ORF screens were performed as previously described6 using the Center for Cancer Systems Biology and Broad Institute Lentiviral Expression Library5. Effects of individual ORFs on drug sensitivity were determined by measuring differential viability (ratio of raw viability in MAPK-pathway inhibitor to viability in dimethylsulphoxide (DMSO)) and subsequently normalized across plates using a z score or standard score. Secondary screens to prioritize identified resistance candidates were performed in eight BRAF(V600E)-mutant melanoma cell lines in a manner similar to the primary screens. Prioritization of candidates was accomplished by generation of a composite rescue score for each gene, representing the extent and breadth of ORF-induced resistance phenotype across cell lines. Further validation and characterization of candidate resistance genes and pathways were accomplished using both biochemical and cell biological approaches. Detailed descriptions of all procedures are included in Methods.

Online Methods

Broad Institute and Center for Cancer Systems Biology Lentiviral Expression Library

The genesis, cloning, sequencing and production of the Broad Institute and Center for Cancer Systems Biology Lentiviral Expression Library have been described previously5. All ORFs described in this manuscript were expressed from pLX304 (http://www.addgene.org/25890), a lentiviral expression vector that encodes a C-terminal V5-epitope tag, a blasticidin resistance gene, and drives ORF expression from a cytomegalovirus (CMV) promoter. All clones described in this manuscript are publicly available through members of the ORFeome collaboration (http://www.orfeomecollaboration.org/).

Genome-scale ORF resistance screens

A375 cells were robotically seeded into 384-well white-walled, clear-bottom plates in RPMI-1640 (cellgro) supplemented with 10% FBS and 1% penicillin/streptomycin. The Broad Institute and Center for Cancer Systems Biology Lentiviral Expression Library5 was arrayed on 47 × 384-well plates, from which virus was robotically transferred to cell plates. Cell plates were randomly divided into six treatment arms in duplicate: DMSO, PLX4720, AZD6244, PLX4720 plus AZD6244, VRT11e or a parallel selection arm (blasticidin). Twenty-four hours after seeding, polybrene was added directly to cells (7.5 µg ml−1 final concentration), followed immediately by robotic addition of the Broad Institute and Center for Cancer Systems Biology virus collection (3 µl per well) and centrifuged at 2250 r.p.m. (1,178g) for 30 min at 37 °C. Following a 24-h incubation at 37 °C (5% CO2), media and virus was aspirated and replaced with complete growth media or media containing blasticidin (10 µg ml−1) to select for ORF expressing cells and to determine infection efficiency. Forty-eight hours after media change, unselected (no blasticidin) cells were treated with DMSO (vehicle control) or MAPK pathway inhibitors to a final concentration of 2 µM (PLX4720, VRT11e) or 200 nM (AZD6244). Identical concentrations used for single-agent PLX4720 and AZD6244 treatment were used for combined PLX4720/AZD6244 treatment. Single-agent inhibitors were balanced with DMSO such that all wells contained 0.033% DMSO. Four days (96 h.) after drug addition, cell viability was assessed via robotic addition of CellTiterGlo (1:6 dilution) followed by 10 min orbital agitation at room temperature and subsequent quantification (EnVision Multilabel Reader, Perkin Elmer). Primary screens were performed in 16 individual batches in which two to three viral stock plates were screened per batch against all compounds.

Identification of resistance candidates from primary screening data

Following quantification of cell viability, duplicate luminescence values were averaged for each ORF within each treatment condition. Per cent rescue capability of each ORF was determined by dividing the average luminescence value in each drug by the average luminescence value in DMSO. Subsequent per cent rescue values were normalized within screening plates using the plate average and standard deviation to generate a z score or standard score of per cent rescue.

To calculate infection efficiency of each ORF, luminescence values in the presence of blasticidin were normalized to the average luminescence in DMSO and expressed as a percentage. ORF-mediated effects on cell viability in the absence of drug were assessed by taking the average luminescence value for each ORF in DMSO and normalizing each value to the plate average and standard deviation (z score).

To identify candidate resistance genes, we first filtered out all wells that had an infection efficiency of less than 65%. To eliminate genes with significant effects on cellular growth in the absence of drug treatment, we then filtered out genes that had a z score in DMSO of greater than 2.0 or less than −2.0. In addition, we eliminated from further analysis wells that showed a replicate variability (in DMSO) of greater than 29.15% (equivalent to >2 standard deviations from the average replicate variability). Following this initial filtering, 14, 457 genes remained for subsequent analysis. Within each drug treatment condition, wells showing replicate variability of >2 standard deviations from the mean variability per drug were eliminated from further analysis. Finally, genes showing a z score of per cent rescue of >2.5 were nominated as resistance gene candidates.

Neutral control genes (19) were nominated from primary screening data by identifying genes across virus plates and screening batches with: high infection efficiency (>98.5%); minimal effects on baseline cell growth (z score of viability in DMSO between −0.5 to 0.5); and a rescue score (z score of per cent rescue) <0.25 (for example, no effect on drug sensitivity or resistance). DNA encoding candidates (169), negative controls (enhanced green fluorescent protein (eGFP), n = 9; HcRed, n = 15; luciferase, n = 16) positive controls (MEK1DD, KRASG12V, MAP3K8/COT) and neutral controls (n = 19) were isolated from the Broad Institute and Center for Cancer Systems Biology expression collection and used to create a validation viral stock distinct from that used in the primary screens.

Drug sensitivity curves in A375 cells expressing candidate ORFs

A375 cells were seeded, infected and drug treated exactly as in primary screens using 4 µl of validation viral stock and concentrations of inhibitors ranging from 10 µM to 100 nM in half-log increments. For combinatorial PLX4720–AZD6244 treatment, a fixed dose of PLX4720 (2 µM) was combined with AZD6244 in doses ranging from 10 µM to 100 nM in half-log increments. Viability was assessed as in the primary screen. Resulting luminescence for each ORF was normalized to luminescence in DMSO (per cent rescue) for each drug and drug concentration. Resulting sensitivity curves for each ORF were log transformed and the area under the curve (AUC) calculated using Prism GraphPad software. The resulting AUC for each candidate and control ORF–drug combination were normalized to that of the negative and neutral controls using a z score (described above). ORFs yielding a z score of >1.96 (P < 0.05) were considered to be validated candidates in this cell line.

Validation screens in additional BRAF(V600E) cell lines

Validation screening in additional BRAF(V600E) melanoma cell lines was performed exactly as in the primary screen, but cell lines were empirically optimized for seeding density and viral dilution. Owing to sensitivity of these cell lines to polybrene and virus exposure, all cell lines except for WM266.4 were treated with polybrene and virus, spun for 1 h at 2,250 r.p.m. (1,178g) followed immediately by complete virus and media removal and change to complete growth media. WM266.4 were treated with polybrene and virus, spun for 30 min at 2,250 RPM (1,178g) and incubated for 24 h before virus and media removal and change to complete growth media 24 h after infection. For experimental determination of infection efficiency, blasticidin (5 µg ml−1) was added 24 h after media change. All drug treatments and viability measurements were performed as in primary screens.

The resulting luminescence values were normalized to DMSO (per cent of DMSO or ‘per cent rescue’). The resulting per cent rescue was normalized to the mean and standard deviation of all negative and neutral controls to yield a z score of per cent rescue. Genes with a z score of per cent rescue of >4 in at least two instances were considered to have validated. ‘composite rescue scores’ were derived by summing the z score of per cent rescue of each gene across all drugs and cell lines. Average composite rescue scores for each protein class were generated by taking the average composite rescue score of all genes within a given protein class.

Phosphorylated ERK and V5 immunoassays

For analysis of ERK phosphorylation, A375 were seeded at 1,500 cells per well in black-walled, clear-bottomed, 384-well plates, virally transduced with all candidates and controls and treated with PLX4720, AZD6244 and combinatorial PLX4720–AZD6244 exactly as in the primary resistance screens. Eighteen hours after drug treatment, media was removed and cells were fixed with 4% formaldehyde and 0.1% Triton X-100 in PBS for 30 min at room temperature. Following removal of fixation solution, cells were washed once with PBS and blocked in blocking buffer (LiCOR) for 1 h at room temperature (21–25 °C) with shaking. After removal of blocking buffer, fixed cells were incubated with primary antibody against ERK phosphorylated at Thr 202/Tyr 204 (Sigma, #M8159, 1:2000) in LiCOR blocking buffer containing 0.1% Tween-20 and for 18 h at 4 °C with shaking. Antibody was removed and wells were washed thrice with 0.1% Tween-20 in water followed by incubation in secondary antibody (IRDye 800CW LiCOR, 1:1,200) and dual cellular stains, including Sapphire700 (LiCOR, 1:1000) and DRAQ5 (Cell Signaling Technology, 1:10,000), all diluted in LiCOR blocking buffer (no detergent) and incubated for 1 h at room temperature with shaking. Secondary antibody or cell stain was removed and washed thrice with 0.1% Tween-20 in water followed by a single wash in PBS. PBS was removed and plates were dried for 10 min at room temperature in the dark followed immediately by imaging on an Odyssey CLx Infrared Scanner. For phosphorylated ERK (pERK) and cellular stain, background was calculated based on signal observed in control wells containing only secondary antibody in blocking buffer and subtracted from each experimental well. Total pERK signal was normalized to total cellular stain for each ORF in each drug condition. The resulting values were subsequently normalized to DMSO (per cent of DMSO) for each ORF per drug condition.

V5 immunostaining for ectopic ORF expression was performed as described for the ERK phosphorylation assay above. In brief, cells were seeded at 3,000-4,000 cells per well and infected in parallel to validation screens. Seventy-two hours after infection, cells were fixed, blocked and stained as described for the pERK assay, instead using an antibody directed against the V5 epitope (Invitrogen, #R96025, 1:5,000, Invitrogen). Subsequent washes, secondary antibody incubations and total cellular staining protocol were identical to those described for the pERK assay, above. V5 and cellular stain (DRAQ5/Sapphire700) intensity were quantified as above, background signal subtracted (determined by signal intensity in uninfected wells with no V5 epitope and stained with secondary antibody, only) and V5 signal intensity normalized to cellular stain intensity.

Detection of GPCR-mediated cyclic AMP production

HEK293T cells were seeded at a density of 2.5 × 105 cells per well in 12-well plates. Twenty-four hours after seeding, cells were transfected with 250 ng of the indicated ORF (pLX304 expression vector) using 3 µl of Fugene6 (Promega) transfection reagent. Forty-seven hours after transfection, cells were treated either with DMSO (1:1,000) or IBMX (30 µM). In addition, forskolin (10 µM) and 100 M IBMX were added as positive controls for indicated time. Cells were subsequently lysed in triton X-100 lysis buffer (Cell Signaling Technology) and resulting lysates split for cAMP enzyme-linked immunosorbant assay (ELISA) (Cell Signaling Technology, #4339) or parallel western blot analysis. cAMP ELISA was performed exactly as per the manufacturer’s recommended protocol. Following quantification the inverse absorbance was calculated and normalized to that of negative control ORFs.

Identification of Cyclic AMP response elements in candidate resistance genes

Gene sets that share a common CREB1, ATF1, ATF2 or JUN DNA response element within ±2 kb of their transcriptional start site (as defined by TRANSFAC, version 7.4, http://www.gene-regulation.com/) were identified and downloaded from the MSigDB website (Extended Data Fig. 8a), available at http://www.broadinstitute.org/gsea/msigdb). CRE-containing genes present in individual gene sets were subsequently identified within the group of screened ORFs and within the group of candidate/neutral control ORFs. The ratio of CRE-containing genes to screened genes (expected) was compared to the ratio of CRE-containing genes to candidate/neutral control genes (actual) across gene sets. A P value for the observed enrichment of CRE-containing genes in the candidate genes over the expected representation within the screening set was calculated using Pearson’s chi-squared test.

Cell lines and reagents

A375, SKMEL28, SKMEL19, UACC62, COLO-679 and WM983b cells were all grown in RPMI-1640 (Cellgro), 10% FBS and 1% penicillin and streptomycin. WM88, G361, SKMEL5, WM266.4, COLO-205 and 293T cells were all grown in DMEM (Cellgro), 10% FBS and 1% penicillin and streptomycin. All cell lines were acquired via the Cancer Cell Line Encyclopedia (http://www.broadinstitute.org/ccle/home), except for SKMEL19, which was a gift from N. Rosen. AZD6244 (PubChem ID: 10127622) was purchased from Selleck Chemicals, PLX4720 (PubChem ID: 24180719) was purchased from Symansis and VRT11e was synthesized by contract based on its published structure8. Forskolin, IBMX (3-Isobutyl-1-methylxanthine), cyclic AMP (cAMP, N6, 2′-O-dibutyryladenosine 3:5-cyclic monophosphate) and α-MSH (α-melanocyte stimulating hormone) were purchased from Sigma. Panobinostat (LBH-589) was purchased from BioVision, Vorinostat (SAHA) and Entinostat (MS-275) from were purchased from Cayman Chemical. All small molecules were dissolved in DMSO.

Pharmacologic growth inhibition assays

Melanoma cell lines were seeded into 384-well, white-walled, clear-bottom plates at the following densities; A375, 500 cells per well; SKMEL19, 1,500 cells per well; SKMEL28, 1,000 cells per well; UACC62, 1,000 cells per well; WM266.4, 1,800 cells per well; G361, 1,200 cells per well; COLO-679, 2,000 cells per well; SKMEL5, 2,000 cells per well; WM983b 1,500 cells per well; WM88 1,800 cells per well; COLO-205 1,500 cells per well. Twenty-four hours after seeding, serial dilutions of the relevant compound were prepared in DMSO to 1000× stocks. Drug stocks were then diluted 1:100 into appropriate growth media and added to cells at a dilution of 1:10 (1× final), yielding drug concentrations ranging from 100 µM to 1 × 10−5 µM, with the final volume of DMSO not exceeding 1%. When indicated, forskolin (10 µM), IBMX (100 µM), dibutyryl cAMP (100 µM) were added concurrent with MAPK-pathway inhibitors. Cells were incubated for 96 h following addition of drug. Cell viability was measured using CellTiterGlo viability assay (Promega). Viability was calculated as a percentage of control (DMSO treated cells). A minimum of six replicates were performed for each cell line and drug combination. Data from growth-inhibition assays were modelled using a nonlinear regression curve fit with a sigmoid dose–response. These curves were displayed and GI50 generated using GraphPad Prism 5 for Windows (GraphPad). Sigmoid-response curves that crossed the 50% inhibition point at or above 1.0 µM or 10.0 µM have GI50 values annotated as >1.0 µM or >10.0 µM, respectively. For single-dose studies, WM266.4 were seeded at 5,000 cells per well in 96-well, white-walled, clear-bottom plates and the identical protocol (above) was followed, using a single dose of indicated drug.

ORF and short hairpin RNA expression methods for experimental studies

Indicated ORFs were expressed from pLX-304 (Blast, V5) lentiviral expression plasmids, whereas shRNAs were expressed from pLKO.1. shRNAs and controls are available through the RNAi Consortium Portal (http://www.broadinstitute.org/rnai/public/) and are identifiable by their sequence and clone ID: shLuc (CTTCGAAATGTCCGTTCGGTT, TRCN0000072243), shMITF_492 (TTAGCCTAGAATCAAGTTATA, TRCN0000329869) and shMITF_573 (CGGGAAACTTGATTGATCTTT, TRCN0000019123). For lentiviral production, 293T cells (1.0 × 106 cells per 6-cm dish) were transfected with 1 µg of pLX-Blast-V5-ORF or pLKO.1-shRNA, 900 ng Δ8.9 (gag, pol) and 100 ng VSV-G using 6 µl Fugene6 transfection reagent (Promega). Viral supernatant was collected 72 h post transfection. WM266.4 were infected at a 1:10–1:20 dilution (ORFs) or 1:100 dilution (shRNA) of virus in 6-well plates (2.0 × 105 cells per well, for immunoblot assays) or 96-well plates (3.0 × 103, for cell growth assays) in the presence of 5.5 µg ml−1 polybrene and centrifuged at 2,250 r.p.m. for 60 min at 37 °C followed immediately by removal of media and replacement with complete growth media. Seventy-two hours after infection, drug treatments/pharmacological perturbations were initiated (see below).

Generation of CREB1 and A-CREB reagents

Wild-type CREB1 (Isoform B, NM_134442.3) was obtained through the Broad Institute RNAi Consortium, a member of the ORFeome Collaboration (http://www.orfeomecollaboration.org/). Arginine 301 of CREB was mutated to Leucine yielding CREB(R301L) (equivalent to CREB(R287L) in isoform A) using the QuikChange Lightning Mutagenesis Kit (Agilent), performed in pDonor223 (Invitrogen). CREB(R301L) was transferred into pLX304 using LR Clonase (Invitrogen) per manufacturer’s recommendation. The A-CREB complementary DNA17 was synthesized (Genewiz) with flanking Gateway recombination sequences, recombined first into pDonor223 and subsequently into pLX304 as described for CREB1 mutant cDNAs.

Quantitative RT/PCR

mRNA was extracted from WM266.4 using the RNeasy kit (Qiagen) and homogenized using the Qiashredder kit (Qiagen). Total mRNA was used for subsequent reverse transcription using the SuperScript III First-Strand Synthesis SuperMix (Invitrogen). Five microlitres of reverse-transcribed cDNA was used for quantitative PCR using SYBR Green PCR Master Mix and gene-specific primers, in quadruplicate, using an ABI PRISM 7900 Real Time PCR System. Primers used for detection were as follows; NR4A2 forward: 5′- GTT CAG GCG CAG TAT GGG TC -3′; NR4A2 reverse: 5′- AGA GTG GTA ACT GTA GCT CTG AG -3′; NR4A1 forward: 5′- ATG CCC TGT ATC CAA GCC C -3′; NR4A1 reverse: 5′- GTG TAG CCG TCC ATG AAG GT -3′; DUSP6 forward: 5′- CTG CCG GGC GTT CTA CCT -3′; DUSP6 reverse: 5′- CCA GCC AAG CAA TGT ACC AAG -3′; MITF forward: 5′- TGC CCA GGC ATG AACACA C-3′; MITF reverse: 5′- TGG GAA AAA TACACG CTG TGA G -3′; FOS forward: 5′- CAC TCC AAG CGG AGA CAG AC -3′; FOS reverse: 5′- AGG TCA TCA GGG ATC TTG CAG -3′; TBP forward: 5′- CCC GAA ACG CCG AAT ATA ATC C -3′; TBP reverse: 5′- GAC TGT TCT TCA CTC TTG GCT C -3′. Relative expression was determined using the comparative CT method (Applied Biosystems) followed by normalization to the DMSO/T0 time point.

Immunoblot analyses and antibodies

Adherent cells were washed once with ice-cold PBS and lysed passively with 1% NP-40 buffer (150 mM NaCl, 50 mM Tris, pH 7.5, 2 mM EDTA, pH 8, 25 mM NaF and 1% NP-40) containing 2× protease inhibitors (Roche) and 1× Phosphatase Inhibitor Cocktails I and II (CalBioChem). Lysates were quantified (Bradford assay), normalized, reduced, denatured (95 °C) and resolved by SDS gel electrophoresis on 4–20% Tris or Glycine gels (Invitrogen). Resolved protein was transferred to nitrocellulose or PVDF membranes, blocked in LiCOR blocking buffer and probed with primary antibodies recognizing MITF (NeoMarkers, Clone C5, #MS-771-P, 1:400), Cyclin D1 (NeoMarkers, Clone Ab-3, #RB-010-P, 1:400), pERK1 and pERK2 (Thr 202/Tyr 204, Sigma, #M8159, 1:5,000), SLVR (Sigma, SAB4100050, 1:500), vinculin (Sigma, V9131, 1:20,000), total MEK1 (BD Transduction, #610122, 1:1000), acetylated histone H3 (Millipore, #06-599, 1:2000) and V5 epitope (Invitrogen, #R96025, 1:5,000). The following antibodies were purchased from Cell Signaling Technology and used at 1:1000 dilution: pMEK1 and pMEK2 (Ser 217/Ser 221, #9154), FOS (#2250), pCREB and pATF1 (Ser 133, Ser 63, respectively, #9196), CREB (#4820) and β-Actin (#3700, 1:20,000). The following antibodies were purchased from Santa Cruz Biotechnology: BCL2 (Clone C-2, sc-7382, 1:250), TRP1 (Clone G-17, sc-10443, 1:1000), Melan-A (Clone A103, sc-20032, 1:1000), ERK2 (Clone C-14, sc-154, 1:5,000), NR4A1/Nur77 (Clone M-210, sc-5569, 1:250), NR4A2/Nurr1 (Clone N-20, sc-991, 1:500), SOX10 (Clone N-20, sc-17342, 1:400). After incubation with the appropriate secondary antibody (anti-rabbit, anti-mouse or anti-goat immunoglobulin G (IgG), IRDye-linked; 1:15,000 dilution; IRDye 800CW, 1:20,000 IRDye 680LT, LiCOR), proteins were imaged and quantified using an Odyssey CLx scanner (LiCOR). Lysates from tumour and matched normal skin were generated by homogenization of tissue in 1% Triton X-100, 50 mM HEPES, pH 7.4, 150 mM NaCl, 1.5 mM MgCl2, 1 mM EGTA, 100 mM NaF, 10 mM Na pyrophosphate, 1 mM Na3VO4, 10% glycerol, containing freshly added protease and phosphatase inhibitors (Roche Applied Science Cat. # 05056489001 and 04906837001, respectively). Subsequent normalization and immunoblot analyses were performed as above.

LINCS analysis

To explore transcriptional connections between cAMP signalling and GPCR-pathway-associated drug-resistance candidates, we expressed all of our candidate and control genes in A375 cells (as described above) and generated gene-expression profiles using a high-throughput Luminex bead-based platform. We queried the LINCS database (http://www.lincscloud.org) using a gene-expression signature of adenyl cyclase stimulation generated by treating A375 cells with colforsin, an adenyl cyclase agonist. We computed the similarity of the colforsin signature to 8729 treatment signatures in the A375 cell line (including the resistance candidate genes) that were available in the database, using a two-tailed weighted enrichment metric (connectivity score). We obtained a ranked list of the treatments based on the strength of the connectivity scores, and examined the ranks of the resistance candidate genes as well as the ranks of neutral control genes.

Expression profiling of melanoma cancer cell lines

We carried out oligonucleotide microarray analysis using the GeneChip Human Genome U133 Plus 2.0 Affymetrix expression array (Affymetrix). Samples were converted to labelled, fragmented, cRNA per the Affymetrix protocol for use on the expression microarray. All expression arrays are available on the Broad-Novartis Cancer Cell Line Encyclopedia data portal at http://www.broadinstitute.org/ccle/home or on the Gene Expression Omnibus (GSE36133).

Melanoma tumour biopsies

Biopsied tumour material consisted of discarded and de-identified tissue that was obtained with informed consent and characterized under institutional review board (IRB) protocol 11-181 (Dana-Farber Cancer Institute). For paired specimens, ‘on-treatment’ samples were collected 10 to 14 days after initiation of MAPK inhibitor treatment (Extended Data Fig. 7e).