Exploration of the MEK/ERK signaling pathway has revealed significant complexity to be considered when modeling response to MEK inhibitors. Functional activation of MEK can be driven from RAF, RAS, or RTKs, and resistance can be mediated by different compensatory mechanisms including alternative RAS/RTK effectors such as PI3K (). This level of pathway interplay highlights the challenge of identifying biomarkers to predict dependence on MEK.
Previous studies have linked BRAF and, more weakly, RAS mutations to in vitro
sensitivity to MEK inhibition (1
) and PI3K pathway–activating mutations to resistance (6
). The results from the present study using selumetinib support this general observation, but reveal these relationships to be far from absolute when assessed across a larger, more diverse collection of cell types (). A similar trend was observed for protein markers of MEK/ERK and PI3K pathway activation, with pERK and pAkt proving to be less robust markers of pathway output than previously suggested (refs. 5
; ). It is perhaps not surprising that individual mutation or protein measurements fail to adequately predict pathway activity considering the complexity of signal control through the MEK/ERK axis. To provide a more comprehensive molecular assessment of pathway status, we set out to identify gene expression networks that more accurately predict sensitivity to MEK inhibition. Furthermore, we used large cell panels to at least partially reflect known heterogeneity in tumor biology and increase the likelihood that in vitro
signatures can be translated into the clinical setting.
By incorporating biological assumptions within the statistical approach taken (; Supplementary Fig. S2
), we prioritized two gene transcription networks as markers of functional output from pathways that act cooperatively to predict response to selumetinib in vitro
. This predictivity was reproducible across independent cell panels of diverse tumor origin, even when profiled in different laboratories using alternative technology platforms (). The largest of these networks comprised 18 genes capturing transcriptional events common to MEK/ERK functional output and has therefore been termed the MEK-functional-activation signature (; Supplementary Fig. S7a
). This signature contains dual-specificity phosphatases (DUSP4/6; refs. 24
), sprouty homologue 2 (SPRY2; refs. 24
), and pleckstrin homology-like domain family A member 1 (PHLDA1; ref. 31
), all of which are known transcriptional targets of MEK/ERK signaling involved in negative feedback regulation of ERK and its upstream modulators. Other known transcriptional targets of MEK/ERK signaling present in the signature are the Ets variant transcription factors (ETV4, ETV5, and ELF1; refs. 32
), alongside other MEK family members (MAP2K3) potentially coactivated by signals activating MEK1/2. The signature also suggests the importance of other genes previously linked to regulation of MAPK signaling, cell cycle, and tumor prognosis, including tribbles 2 (TRIB2; ref. 34
), galectin 3 (LGALS3; ref. 35
), and the transcription factors KANK1 (ANKRD15) and leucine zipper (LZ-) TS1 (36
mutation () and pERK protein measurements () vary across cells that respond to selumetinib, expression of the MEK-functional-activation signature is consistently high (–; Supplementary Fig. S10
). Furthermore, expression of this signature is dynamically increased following MEK activation (28
) and decreased following MEK inhibition in multiple tumor cell lines and xenografts (ref. 24
; ). Collectively, these data show the biologically relevant and robust measurement of MEK pathway output and inhibition given by this signature, independent of the pathway activation point, highlighting its utility as both a predictor of drug sensitivity and a marker of pharmacodynamic response. Because the MEK pathway can be functional in cells that display resistance to MEK inhibition, this signature may also enable a more rational selection of preclinical models (and perhaps patients) in which to test drug combinations (Supplementary Fig. S12
), especially if the nature of the compensatory pathways that mask MEK dependence can be identified.
The second network identified was reproducibly predictive of resistance in cells with MEK functional activity across independent cell panels and was termed compensatory-resistance (). Biological overlay suggested that this signal may be the result of a branch in signaling upstream of RAF/MEK, with consistent transcriptional regulation by RAS seen for the majority of these genes (, Document S1
). This hypothesis was supported as expression of the compensatory-resistance signature was low in BRAF-mutant cells (; Supplementary Fig. S9B
) and was not seen without MEK activity (). The signature comprises a diverse set of genes with common linkage to transforming growth factor-β (TGF-β)/tumor necrosis factor-α (TNF-α)/NF-κB signaling (; Supplementary Fig. S7B
; refs. 38
). A number of these genes are known to regulate signaling pathways that offer an alternative route to cell proliferation, for example, activation of the G-protein–coupled receptor frizzled homolog 2 (FZD2), which activates WNT signaling (40
), or activation of Jak-STAT by interleukin-6 (IL-6; ref. 41
). Alongside these are a number of genes potentially offering enhanced cell survival and chemoresistance through control of tumorigenic processes such as hypoxia/angiogenesis (serine protease SERPINE1, lysyl oxidase LOX, and collagens COL5A1 and COL12A1), cell cycle [G0
switch 2 (GOS2)], proliferation/apoptisis [cysteine-rich transmembrane BMP regulator 1 (CRIM1; ref. 42
) and clusterin (CLU; ref. 43
)], and immune evasion (CD274; ref. 44
The implication that, where MEK is active, Ras effector signaling through PI3K may mediate resistance to MEK inhibition is not new (6
). Surprisingly, however, expression of the compensatory-resistance signature seemed to be independent of PI3K pathway activation (; Supplementary Fig. S8B
), contradicting the literature precedent that PI3K activity alone may be the primary determinant of resistance (6
). Where MEK activity is driven from a point upstream of RAF, expression from this compensatory-resistance signature potentially enables better separation of cells with lower MEK dependence.
Having assembled these transcript networks and shown their in vitro
predictive power and ability to recapitulate known biology, we sought to assess their potential as biomarkers in the clinical setting. We showed that the MEK-functional-activation and compensatory-resistance signatures can be reliably detected in fixed clinical tissue using a single RT-qPCR–based test and that the internal correlation structure of these gene networks is preserved. Furthermore, we showed the expression of the MEK-functional-activation signature to be higher in BRAF-mutant than in WT melanoma (), indicating that detectable transcriptional wiring is comparable between preclinical and clinical samples. From these data, we believe that it is feasible to use a single test measuring mRNA signatures as an investigative predictive biomarker in clinical trials for MEK-targeted therapies. A key challenge in this context will be the translation of gene expression thresholds set by preclinical data to give clinically relevant patient selection. It is likely that a training step will be necessary to first optimize the aggregation and application of gene signatures to suit the tissue type being measured () and the gene expression platform being used (). Considerations also need to be taken when designing broader application of such a test in the clinic, with standardized operating procedures necessary to control for the confounding effects of variables such as differing protocols, age of samples, and fixation methods (47
In summary, the methods outlined in this article aim to improve the biological and clinical relevance of hypotheses generated from preclinical gene expression data. We identified transcript signatures, robustly detectable using a single test in fixed clinical tissue, enabling enhanced measurement of baseline/dynamic functional activity from MEK and prediction of response to selumetinib. These signatures can identify MEK dependency irrespective of the genetic or cell-specific factors that determine signaling preferences, particularly in cells where MEK is only one output of a more pleiotropic upstream signal (e.g., RAS mutation). Although we have focused on identifying transcriptome signatures related to response to MEK inhibition, the approach is equally applicable if extended to other drug targets and signaling pathways, particularly where genetic markers of response are unknown or insufficient to capture complex signaling.